auto-generating sphinx docs

Author: pytorchbot

docs/master/__config__.html

@@ -159,7 +159,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/index.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 
@@ -614,7 +614,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_storage_classes</span></div>
 
 
-<span class="k">def</span> <span class="nf">set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
+<div class="viewcode-block" id="set_default_tensor_type"><a class="viewcode-back" href="../generated/torch.set_default_tensor_type.html#torch.set_default_tensor_type">[docs]</a><span class="k">def</span> <span class="nf">set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Sets the default ``torch.Tensor`` type to floating point tensor type</span>
 <span class="sd">    ``t``. This type will also be used as default floating point type for</span>
 <span class="sd">    type inference in :func:`torch.tensor`.</span>
@@ -635,10 +635,10 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">_string_classes</span><span class="p">):</span>
         <span class="n">t</span> <span class="o">=</span> <span class="n">_import_dotted_name</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
-    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>
 
 
-<span class="k">def</span> <span class="nf">set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
+<div class="viewcode-block" id="set_default_dtype"><a class="viewcode-back" href="../generated/torch.set_default_dtype.html#torch.set_default_dtype">[docs]</a><span class="k">def</span> <span class="nf">set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Sets the default floating point dtype to :attr:`d`.</span>
 <span class="sd">    This dtype is:</span>
 
@@ -666,9 +666,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">        torch.complex128</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span></div>
 
-<span class="k">def</span> <span class="nf">set_deterministic</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
+<div class="viewcode-block" id="set_deterministic"><a class="viewcode-back" href="../generated/torch.set_deterministic.html#torch.set_deterministic">[docs]</a><span class="k">def</span> <span class="nf">set_deterministic</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot; Sets whether PyTorch operations must use &quot;deterministic&quot;</span>
 <span class="sd">    algorithms. That is, algorithms which, given the same input, and when</span>
 <span class="sd">    run on the same software and hardware, always produce the same output.</span>
@@ -740,7 +740,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">        d (:class:`bool`): If True, force operations to be deterministic.</span>
 <span class="sd">                           If False, allow non-deterministic operations.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic</span><span class="p">(</span><span class="n">d</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="is_deterministic"><a class="viewcode-back" href="../generated/torch.is_deterministic.html#torch.is_deterministic">[docs]</a><span class="k">def</span> <span class="nf">is_deterministic</span><span class="p">():</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if the global deterministic flag is turned on. Refer to</span>
@@ -760,8 +760,8 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
     <span class="k">pass</span>
 
 
-<div class="viewcode-block" id="FloatStorage"><a class="viewcode-back" href="../storage.html#torch.FloatStorage">[docs]</a><span class="k">class</span> <span class="nc">FloatStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">FloatStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
-    <span class="k">pass</span></div>
+<span class="k">class</span> <span class="nc">FloatStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">FloatStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
+    <span class="k">pass</span>
 
 
 <span class="k">class</span> <span class="nc">HalfStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">HalfStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
@@ -928,9 +928,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="k">del</span> <span class="n">_torch_docs</span><span class="p">,</span> <span class="n">_tensor_docs</span><span class="p">,</span> <span class="n">_storage_docs</span>
 
 
-<span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
+<div class="viewcode-block" id="compiled_with_cxx11_abi"><a class="viewcode-back" href="../generated/torch.compiled_with_cxx11_abi.html#torch.compiled_with_cxx11_abi">[docs]</a><span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1&quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span>
+    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span></div>
 
 
 <span class="c1"># Import the ops &quot;namespace&quot;</span>

docs/master/_modules/torch/__config__.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_jit_internal.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_lobpcg.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_lowrank.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_tensor_str.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_utils.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_vmap_internals.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/autograd.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/autograd/anomaly_mode.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/autograd/function.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/autograd/functional.html

@@ -158,7 +158,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+91256aa &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ec85071 &#x25BC</a>
                 </div>
 
 
@@ -728,15 +728,15 @@ <h1>Source code for torch.autograd.functional</h1><div class="highlight"><pre>
 <span class="sd">            Defaults to ``False``.</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        Jacobian (Tensor or nested tuple of Tensors): if there are a single</span>
-<span class="sd">            input and output, this will be a single Tensor containing the</span>
-<span class="sd">            Jacobian for the linearized inputs and output. If one of the two is</span>
-<span class="sd">            a tuple, then the Jacobian will be a tuple of Tensors. If both of</span>
-<span class="sd">            them are tuples, then the Jacobian will be a tuple of tuple of</span>
-<span class="sd">            Tensors where ``Jacobian[i][j]`` will contain the Jacobian of the</span>
-<span class="sd">            ``i``\th output and ``j``\th input and will have as size the</span>
-<span class="sd">            concatenation of the sizes of the corresponding output and the</span>
-<span class="sd">            corresponding input.</span>
+<span class="sd">        Jacobian (Tensor or nested tuple of Tensors): if there is a single</span>
+<span class="sd">        input and output, this will be a single Tensor containing the</span>
+<span class="sd">        Jacobian for the linearized inputs and output. If one of the two is</span>
+<span class="sd">        a tuple, then the Jacobian will be a tuple of Tensors. If both of</span>
+<span class="sd">        them are tuples, then the Jacobian will be a tuple of tuple of</span>
+<span class="sd">        Tensors where ``Jacobian[i][j]`` will contain the Jacobian of the</span>
+<span class="sd">        ``i``\th output and ``j``\th input and will have as size the</span>
+<span class="sd">        concatenation of the sizes of the corresponding output and the</span>
+<span class="sd">        corresponding input.</span>
 
 <span class="sd">    Example:</span>
 
@@ -823,12 +823,12 @@ <h1>Source code for torch.autograd.functional</h1><div class="highlight"><pre>
 <span class="sd">            Defaults to ``False``.</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        Hessian (Tensor or a tuple of tuple of Tensors) if there are a single input,</span>
-<span class="sd">            this will be a single Tensor containing the Hessian for the input.</span>
-<span class="sd">            If it is a tuple, then the Hessian will be a tuple of tuples where</span>
-<span class="sd">            ``Hessian[i][j]`` will contain the Hessian of the ``i``\th input</span>
-<span class="sd">            and ``j``\th input with size the sum of the size of the ``i``\th input plus</span>
-<span class="sd">            the size of the ``j``\th input.</span>
+<span class="sd">        Hessian (Tensor or a tuple of tuple of Tensors): if there is a single input,</span>
+<span class="sd">        this will be a single Tensor containing the Hessian for the input.</span>
+<span class="sd">        If it is a tuple, then the Hessian will be a tuple of tuples where</span>
+<span class="sd">        ``Hessian[i][j]`` will contain the Hessian of the ``i``\th input</span>
+<span class="sd">        and ``j``\th input with size the sum of the size of the ``i``\th input plus</span>
+<span class="sd">        the size of the ``j``\th input.</span>
 
 <span class="sd">    Example:</span>
 
@@ -1007,7 +1007,9 @@ <h1>Source code for torch.autograd.functional</h1><div class="highlight"><pre>
 <span class="sd">            hvp for said inputs, which is the expected mathematical value.</span>
 <span class="sd">            Defaults to ``False``.</span>
 <span class="sd">    Returns:</span>
-<span class="sd">        func_output (tuple of Tensors or Tensor): output of ``func(inputs)``</span>
+<span class="sd">        output (tuple): tuple with:</span>
+<span class="sd">            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``</span>
+
 <span class="sd">            hvp (tuple of Tensors or Tensor): result of the dot product with</span>
 <span class="sd">            the same shape as the inputs.</span>