Generate Python docs from pytorch/pytorch@0d16189

Author: pytorchbot

docs/master/_images/RReLU.png

@@ -1223,9 +1223,6 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">__future__</span> <span class="k">as</span> <span class="n">__future__</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">profiler</span> <span class="k">as</span> <span class="n">profiler</span>
 
-<span class="kn">from</span> <span class="nn">torch.nested._nestedtensor</span> <span class="kn">import</span> <span class="n">NestedTensor</span>
-<span class="kn">from</span> <span class="nn">torch.nested._nestedtensor</span> <span class="kn">import</span> <span class="n">nested_tensor</span>
-
 <span class="n">_C</span><span class="o">.</span><span class="n">_init_names</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="p">))</span>
 
 <span class="c1"># attach docstrings to torch and tensor functions</span>

docs/master/_modules/torch.html

@@ -740,7 +740,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
         <span class="c1"># All strings are unicode in Python 3.</span>
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_tensor_str</span><span class="o">.</span><span class="n">_str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 
-<div class="viewcode-block" id="Tensor.backward"><a class="viewcode-back" href="../../generated/torch.Tensor.backward.html#torch.Tensor.backward">[docs]</a>    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Computes the gradient of current tensor w.r.t. graph leaves.</span>
 
 <span class="sd">        The graph is differentiated using the chain rule. If the tensor is</span>
@@ -796,7 +796,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
                 <span class="n">retain_graph</span><span class="o">=</span><span class="n">retain_graph</span><span class="p">,</span>
                 <span class="n">create_graph</span><span class="o">=</span><span class="n">create_graph</span><span class="p">,</span>
                 <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">)</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">)</span></div>
+        <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">register_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hook</span><span class="p">):</span>
         <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Registers a backward hook.</span>
@@ -933,7 +933,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">norm</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,),</span> <span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="n">keepdim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">keepdim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">lu</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">get_infos</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="Tensor.lu"><a class="viewcode-back" href="../../generated/torch.Tensor.lu.html#torch.Tensor.lu">[docs]</a>    <span class="k">def</span> <span class="nf">lu</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">get_infos</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sa">r</span><span class="sd">&quot;&quot;&quot;See :func:`torch.lu`&quot;&quot;&quot;</span>
         <span class="c1"># If get_infos is True, then we don&#39;t need to check for errors and vice versa</span>
         <span class="k">if</span> <span class="n">has_torch_function_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -943,7 +943,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">get_infos</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">LU</span><span class="p">,</span> <span class="n">pivots</span><span class="p">,</span> <span class="n">infos</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">LU</span><span class="p">,</span> <span class="n">pivots</span>
+            <span class="k">return</span> <span class="n">LU</span><span class="p">,</span> <span class="n">pivots</span></div>
 
     <span class="k">def</span> <span class="nf">stft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">hop_length</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
              <span class="n">win_length</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">window</span><span class="p">:</span> <span class="s1">&#39;Optional[Tensor]&#39;</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>