Generate Python docs from pytorch/pytorch@49244d5

Author: pytorchbot

docs/master/__config__.html

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docs/master/_modules/index.html

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docs/master/_modules/torch.html

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docs/master/_modules/torch/__config__.html

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docs/master/_modules/torch/_jit_internal.html

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docs/master/_modules/torch/_lobpcg.html

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docs/master/_modules/torch/_tensor.html

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@@ -1315,7 +1315,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
                           <span class="s2">&quot;attribute won&#39;t be populated during autograd.backward(). If you indeed want the gradient &quot;</span>
                           <span class="s2">&quot;for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the &quot;</span>
                           <span class="s2">&quot;non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See &quot;</span>
-                          <span class="s2">&quot;github.com/pytorch/pytorch/pull/30531 for more informations.&quot;</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+                          <span class="s2">&quot;github.com/pytorch/pytorch/pull/30531 for more information.&quot;</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span>
 
     <span class="nd">@grad</span><span class="o">.</span><span class="n">setter</span>

docs/master/_modules/torch/_tensor_str.html

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docs/master/_modules/torch/_utils.html

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docs/master/_modules/torch/autograd.html

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@@ -455,7 +455,7 @@ <h1>Source code for torch.autograd</h1><div class="highlight"><pre>
     <span class="n">retain_graph</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">create_graph</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
     <span class="n">grad_variables</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_TensorOrTensors</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_TensorOrTensors</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Computes the sum of gradients of given tensors w.r.t. graph leaves.</span>
 
@@ -487,24 +487,24 @@ <h1>Source code for torch.autograd</h1><div class="highlight"><pre>
 <span class="sd">        :ref:`Stream semantics of backward passes&lt;bwd-cuda-stream-semantics&gt;`.</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        tensors (sequence of Tensor): Tensors of which the derivative will be</span>
+<span class="sd">        tensors (Sequence[Tensor] or Tensor): Tensors of which the derivative will be</span>
 <span class="sd">            computed.</span>
-<span class="sd">        grad_tensors (sequence of (Tensor or None)): The &quot;vector&quot; in the Jacobian-vector</span>
-<span class="sd">            product, usually gradients w.r.t. each element of corresponding tensors.</span>
-<span class="sd">            None values can be specified for scalar Tensors or ones that don&#39;t require</span>
-<span class="sd">            grad. If a None value would be acceptable for all grad_tensors, then this</span>
-<span class="sd">            argument is optional.</span>
+<span class="sd">        grad_tensors (Sequence[Tensor or None] or Tensor, optional): The &quot;vector&quot; in</span>
+<span class="sd">            the Jacobian-vector product, usually gradients w.r.t. each element of</span>
+<span class="sd">            corresponding tensors. None values can be specified for scalar Tensors or</span>
+<span class="sd">            ones that don&#39;t require grad. If a None value would be acceptable for all</span>
+<span class="sd">            grad_tensors, then this argument is optional.</span>
 <span class="sd">        retain_graph (bool, optional): If ``False``, the graph used to compute the grad</span>
 <span class="sd">            will be freed. Note that in nearly all cases setting this option to ``True``</span>
 <span class="sd">            is not needed and often can be worked around in a much more efficient</span>
 <span class="sd">            way. Defaults to the value of ``create_graph``.</span>
 <span class="sd">        create_graph (bool, optional): If ``True``, graph of the derivative will</span>
 <span class="sd">            be constructed, allowing to compute higher order derivative products.</span>
 <span class="sd">            Defaults to ``False``.</span>
-<span class="sd">        inputs (sequence of Tensor): Inputs w.r.t. which the gradient will be</span>
-<span class="sd">            accumulated into ``.grad``. All other Tensors will be ignored. If not</span>
-<span class="sd">            provided, the gradient is accumulated into all the leaf Tensors that were</span>
-<span class="sd">            used to compute the attr::tensors. All the provided inputs must be leaf</span>
+<span class="sd">        inputs (Sequence[Tensor] or Tensor, optional): Inputs w.r.t. which the gradient</span>
+<span class="sd">            be will accumulated into ``.grad``. All other Tensors will be ignored. If</span>
+<span class="sd">            not provided, the gradient is accumulated into all the leaf Tensors that</span>
+<span class="sd">            were used to compute the attr::tensors. All the provided inputs must be leaf</span>
 <span class="sd">            Tensors.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="n">grad_variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@@ -519,7 +519,8 @@ <h1>Source code for torch.autograd</h1><div class="highlight"><pre>
         <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;&#39;inputs&#39; argument to backward() cannot be empty.&quot;</span><span class="p">)</span>
 
     <span class="n">tensors</span> <span class="o">=</span> <span class="p">(</span><span class="n">tensors</span><span class="p">,)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>
-    <span class="n">inputs</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">()</span>
+    <span class="n">inputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> \
+        <span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">()</span>
 
     <span class="n">grad_tensors_</span> <span class="o">=</span> <span class="n">_tensor_or_tensors_to_tuple</span><span class="p">(</span><span class="n">grad_tensors</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="p">))</span>
     <span class="n">grad_tensors_</span> <span class="o">=</span> <span class="n">_make_grads</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">grad_tensors_</span><span class="p">)</span>

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