Generate Python docs from pytorch/pytorch@8d56858

Author: pytorchbot

docs/master/__config__.html

@@ -188,7 +188,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/index.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 
@@ -627,7 +627,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">module</span> <span class="o">+</span> <span class="n">class_name</span>
 
 
-<span class="k">def</span> <span class="nf">is_tensor</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
+<div class="viewcode-block" id="is_tensor"><a class="viewcode-back" href="../generated/torch.is_tensor.html#torch.is_tensor">[docs]</a><span class="k">def</span> <span class="nf">is_tensor</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if `obj` is a PyTorch tensor.</span>
 
 <span class="sd">    Note that this function is simply doing ``isinstance(obj, Tensor)``.</span>
@@ -638,19 +638,19 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">    Args:</span>
 <span class="sd">        obj (Object): Object to test</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</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">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span></div>
 
 
-<span class="k">def</span> <span class="nf">is_storage</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
+<div class="viewcode-block" id="is_storage"><a class="viewcode-back" href="../generated/torch.is_storage.html#torch.is_storage">[docs]</a><span class="k">def</span> <span class="nf">is_storage</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if `obj` is a PyTorch storage object.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        obj (Object): Object to test</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <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>
+    <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>
 
 
-<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="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>
@@ -671,10 +671,10 @@ <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></div>
+    <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 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="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>
 
@@ -702,9 +702,9 @@ <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></div>
+    <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="k">def</span> <span class="nf">use_deterministic_algorithms</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
+<div class="viewcode-block" id="use_deterministic_algorithms"><a class="viewcode-back" href="../generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms">[docs]</a><span class="k">def</span> <span class="nf">use_deterministic_algorithms</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>
@@ -781,7 +781,7 @@ <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_algorithms</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_algorithms</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>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;This function is deprecated and will be removed in a future release.</span>

docs/master/_modules/torch/__config__.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_jit_internal.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 
@@ -573,6 +573,9 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 <span class="c1"># functions will be defined at a global scope like MyGlobalClass. In cases</span>
 <span class="c1"># where they are not, it is possible to work around issues by declaring the</span>
 <span class="c1"># values global in the function.</span>
+<span class="c1"># In Python 3.9 declaring class as global will make it invisible to</span>
+<span class="c1"># `inspect.getsource`, see https://bugs.python.org/issue42666 .</span>
+<span class="c1"># This could be worked around by manualy adding it to `global()` dictionary.</span>
 
 
 
@@ -825,7 +828,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">fn</span></div>
 
 
-<div class="viewcode-block" id="unused"><a class="viewcode-back" href="../../generated/torch.jit.unused.html#torch.jit.unused">[docs]</a><span class="k">def</span> <span class="nf">unused</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">unused</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    This decorator indicates to the compiler that a function or method should</span>
 <span class="sd">    be ignored and replaced with the raising of an exception. This allows you</span>
@@ -872,9 +875,9 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">prop</span>
 
     <span class="n">fn</span><span class="o">.</span><span class="n">_torchscript_modifier</span> <span class="o">=</span> <span class="n">FunctionModifiers</span><span class="o">.</span><span class="n">UNUSED</span>
-    <span class="k">return</span> <span class="n">fn</span></div>
+    <span class="k">return</span> <span class="n">fn</span>
 
-<span class="k">def</span> <span class="nf">ignore</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<div class="viewcode-block" id="ignore"><a class="viewcode-back" href="../../generated/torch.jit.ignore.html#torch.jit.ignore">[docs]</a><span class="k">def</span> <span class="nf">ignore</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    This decorator indicates to the compiler that a function or method should</span>
 <span class="sd">    be ignored and left as a Python function. This allows you to leave code in</span>
@@ -965,7 +968,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">fn</span><span class="o">.</span><span class="n">_torchscript_modifier</span> <span class="o">=</span> <span class="n">FunctionModifiers</span><span class="o">.</span><span class="n">IGNORE</span>
         <span class="k">return</span> <span class="n">fn</span>
-    <span class="k">return</span> <span class="n">decorator</span>
+    <span class="k">return</span> <span class="n">decorator</span></div>
 
 
 <span class="k">def</span> <span class="nf">_copy_to_script_wrapper</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>

docs/master/_modules/torch/_lobpcg.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_lowrank.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_tensor_str.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_utils.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_vmap_internals.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+adf8b7e &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+8d56858 &#x25BC</a>
                 </div>