Deploying to main from @ ivy-llc/ivy@29ee162 🚀

ivy/docs/data_classes/data_classes/array/ivy.data_classes.array.experimental.html

@@ -4850,8 +4850,8 @@
 <li><p><strong>modes</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>]], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – None or int list, optional, default is None</p></li>
 <li><p><strong>transpose</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>], default: <code class="docutils literal notranslate"><span class="pre">False</span></code>) – If True, the matrices or vectors in in the list are transposed.
 For complex tensors, the conjugate transpose is used.</p></li>
-<li><p><strong>out</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Array</span></code>], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – optional output array, for writing the result to. It must have a shape that the
-result can broadcast to.</p></li>
+<li><p><strong>out</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Array</span></code>], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – optional output array, for writing the result to.
+It must have a shape that the result can broadcast to.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type<span class="colon">:</span></dt>
@@ -4863,7 +4863,7 @@
 </dl>
 <p class="rubric">Notes</p>
 <p>If no modes are specified, just assumes there is one matrix or vector per mode and returns:
-<span class="math notranslate nohighlight">\(\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }\)</span> # noqa</p>
+<span class="math notranslate nohighlight">\(\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }\)</span></p>
 </dd></dl>
 
 <dl class="py method">

ivy/docs/data_classes/data_classes/factorized_tensor/ivy.data_classes.factorized_tensor.cp_tensor.html

@@ -1431,10 +1431,10 @@
 <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">cp_lstsq_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cp_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/unifyai/ivy/blob/main/ivy/data_classes/factorized_tensor/cp_tensor.py#L405-L464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ivy.data_classes.factorized_tensor.cp_tensor.CPTensor.cp_lstsq_grad" title="Link to this definition">#</a></dt>
 <dd><p>Compute (for a third-order tensor)</p>
 <div class="math notranslate nohighlight">
-\[\begin{split}\nabla 0.5 ||\\mathcal{X} - [\\mathbf{w}; \\mathbf{A}, \\mathbf{B}, \\mathbf{C}]||^2 # noqa\end{split}\]</div>
+\[\begin{split}\nabla 0.5 ||\\mathcal{X} - [\\mathbf{w}; \\mathbf{A}, \\mathbf{B}, \\mathbf{C}]||^2\end{split}\]</div>
 <p>where <span class="math notranslate nohighlight">\([\\mathbf{w}; \\mathbf{A}, \\mathbf{B}, \\mathbf{C}]\)</span>
 is the CP decomposition with weights
-<span class="math notranslate nohighlight">\(\\mathbf{w}\)</span> and factor matrices <span class="math notranslate nohighlight">\(\\mathbf{A}\)</span>, <span class="math notranslate nohighlight">\(\\mathbf{B}\)</span> and <span class="math notranslate nohighlight">\(\\mathbf{C}\)</span>. # noqa
+<span class="math notranslate nohighlight">\(\\mathbf{w}\)</span> and factor matrices <span class="math notranslate nohighlight">\(\\mathbf{A}\)</span>, <span class="math notranslate nohighlight">\(\\mathbf{B}\)</span> and <span class="math notranslate nohighlight">\(\\mathbf{C}\)</span>.
 Note that this does not return the gradient
 with respect to the weights even if CP is normalized.</p>
 <dl class="field-list simple">

ivy/docs/data_classes/data_classes/factorized_tensor/ivy.data_classes.factorized_tensor.tt_tensor.html

@@ -1459,7 +1459,7 @@
 <dt class="sig sig-object py" id="ivy.data_classes.factorized_tensor.tt_tensor.TTTensor.tt_to_tensor">
 <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">tt_to_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">factors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/unifyai/ivy/blob/main/ivy/data_classes/factorized_tensor/tt_tensor.py#L107-L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ivy.data_classes.factorized_tensor.tt_tensor.TTTensor.tt_to_tensor" title="Link to this definition">#</a></dt>
 <dd><p>Return the full tensor whose TT decomposition is given by ‘factors’.</p>
-<p>Re-assembles ‘factors’, which represent a tensor in TT/Matrix-Product-State format # noqa: E501
+<p>Re-assembles ‘factors’, which represent a tensor in TT/Matrix-Product-State format
 into the corresponding full tensor</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1523,8 +1523,8 @@
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>tensor_shape</strong> – shape of the tensor to decompose</p></li>
 <li><p><strong>rank</strong> (default: <code class="docutils literal notranslate"><span class="pre">'same'</span></code>) – way to determine the rank, by default ‘same’
-if ‘same’: rank is computed to keep the number of parameters (at most) the same # noqa: E501
-if float, computes a rank so as to keep rank percent of the original number of parameters # noqa: E501
+if ‘same’: rank is computed to keep the number of parameters (at most) the same
+if float, computes a rank so as to keep rank percent of the original number of parameters
 if int or tuple, just returns rank</p></li>
 <li><p><strong>constant_rank</strong> (default: <code class="docutils literal notranslate"><span class="pre">False</span></code>) – if True, the <em>same</em> rank will be chosen for each modes
 if False (default), the rank of each mode will be

ivy/docs/functional/ivy/experimental/ivy.functional.ivy.experimental.layers.html

@@ -2422,7 +2422,7 @@ <h1>Layers<a class="headerlink" href="#layers" title="Link to this heading">#</a
 <div><p>[[16., 17., 18., 19.]]])</p>
 </div></blockquote>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ivy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">24.</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">ivy</span><span class="o">.</span><span class="n">max_pool1d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)],</span> <span class="n">data_format</span><span class="o">=</span><span class="s2">&quot;NCW&quot;</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span> <span class="c1"># noqa</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">ivy</span><span class="o">.</span><span class="n">max_pool1d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)],</span> <span class="n">data_format</span><span class="o">=</span><span class="s2">&quot;NCW&quot;</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
 <span class="go">ivy.array([[[ 1.,  3.],</span>
 <span class="go">        [ 5.,  7.],</span>
 <span class="go">        [ 9., 11.]],</span>

ivy/docs/functional/ivy/experimental/ivy.functional.ivy.experimental.linear_algebra.html

@@ -2132,7 +2132,7 @@ <h1>Linear algebra<a class="headerlink" href="#linear-algebra" title="Link to th
 </dl>
 <p class="rubric">Notes</p>
 <p>If no modes are specified, just assumes there is one matrix or vector per mode and returns:
-<span class="math notranslate nohighlight">\(\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }\)</span> # noqa</p>
+<span class="math notranslate nohighlight">\(\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }\)</span></p>
 </dd></dl>
 
 <dl class="py function">

ivy/docs/functional/ivy/experimental/layers/ivy.functional.ivy.experimental.layers.max_pool1d.html

@@ -1436,7 +1436,7 @@ <h1>max_pool1d<a class="headerlink" href="#max-pool1d" title="Link to this headi
 <div><p>[[16., 17., 18., 19.]]])</p>
 </div></blockquote>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ivy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">24.</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">ivy</span><span class="o">.</span><span class="n">max_pool1d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)],</span> <span class="n">data_format</span><span class="o">=</span><span class="s2">&quot;NCW&quot;</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span> <span class="c1"># noqa</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">ivy</span><span class="o">.</span><span class="n">max_pool1d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)],</span> <span class="n">data_format</span><span class="o">=</span><span class="s2">&quot;NCW&quot;</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
 <span class="go">ivy.array([[[ 1.,  3.],</span>
 <span class="go">        [ 5.,  7.],</span>
 <span class="go">        [ 9., 11.]],</span>

ivy/docs/functional/ivy/experimental/linear_algebra/ivy.functional.ivy.experimental.linear_algebra.multi_mode_dot.html

@@ -1410,7 +1410,7 @@ <h1>multi_mode_dot<a class="headerlink" href="#multi-mode-dot" title="Link to th
 </dl>
 <p class="rubric">Notes</p>
 <p>If no modes are specified, just assumes there is one matrix or vector per mode and returns:
-<span class="math notranslate nohighlight">\(\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }\)</span> # noqa</p>
+<span class="math notranslate nohighlight">\(\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }\)</span></p>
 </dd></dl>
 
 <dl class="py method">
@@ -1429,8 +1429,8 @@ <h1>multi_mode_dot<a class="headerlink" href="#multi-mode-dot" title="Link to th
 <li><p><strong>modes</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>]], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – None or int list, optional, default is None</p></li>
 <li><p><strong>transpose</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>], default: <code class="docutils literal notranslate"><span class="pre">False</span></code>) – If True, the matrices or vectors in in the list are transposed.
 For complex tensors, the conjugate transpose is used.</p></li>
-<li><p><strong>out</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Array</span></code>], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – optional output array, for writing the result to. It must have a shape that the
-result can broadcast to.</p></li>
+<li><p><strong>out</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Array</span></code>], default: <code class="docutils literal notranslate"><span class="pre">None</span></code>) – optional output array, for writing the result to.
+It must have a shape that the result can broadcast to.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type<span class="colon">:</span></dt>
@@ -1442,7 +1442,7 @@ <h1>multi_mode_dot<a class="headerlink" href="#multi-mode-dot" title="Link to th
 </dl>
 <p class="rubric">Notes</p>
 <p>If no modes are specified, just assumes there is one matrix or vector per mode and returns:
-<span class="math notranslate nohighlight">\(\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }\)</span> # noqa</p>
+<span class="math notranslate nohighlight">\(\\text{x  }\\times_0 \\text{ matrix or vec list[0] }\\times_1 \\cdots \\times_n \\text{ matrix or vec list[n] }\)</span></p>
 </dd></dl>
 
 <dl class="py method">

ivy/docs/functional/ivy/ivy.functional.ivy.losses.html

@@ -1429,7 +1429,7 @@ <h1>Losses<a class="headerlink" href="#losses" title="Link to this heading">#</a
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ivy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">ivy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">2.6</span><span class="p">,</span> <span class="mf">6.2</span><span class="p">,</span> <span class="mf">3.7</span><span class="p">,</span> <span class="mf">5.3</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pos_weight</span> <span class="o">=</span> <span class="n">ivy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">z</span> <span class="o">=</span> <span class="n">ivy</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">pos_weight</span><span class="o">=</span><span class="n">pos_weight</span><span class="p">,</span> <span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># noqa: E501</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">z</span> <span class="o">=</span> <span class="n">ivy</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">pos_weight</span><span class="o">=</span><span class="n">pos_weight</span><span class="p">,</span> <span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="go">ivy.array([8.05393649])</span>
 </pre></div>
 </div>