Generate Python docs from pytorch/pytorch@f6e7a2a

Author: pytorchbot

docs/master/_images/RReLU.png

@@ -1266,6 +1266,9 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
 <span class="sd">        Returns the type of the underlying storage.</span>
 
 <span class="sd">        &quot;&quot;&quot;</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>
+            <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">storage_type</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">storage</span><span class="p">()</span><span class="o">.</span><span class="n">_get_legacy_storage_class</span><span class="p">()</span>
 
     <span class="k">def</span> <span class="nf">refine_names</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">names</span><span class="p">):</span>

docs/master/_modules/torch/_tensor.html

@@ -446,7 +446,7 @@ <h1>Source code for torch.cuda.random</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">results</span></div>
 
 
-<span class="k">def</span> <span class="nf">set_rng_state</span><span class="p">(</span><span class="n">new_state</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<div class="viewcode-block" id="set_rng_state"><a class="viewcode-back" href="../../../generated/torch.cuda.set_rng_state.html#torch.cuda.set_rng_state">[docs]</a><span class="k">def</span> <span class="nf">set_rng_state</span><span class="p">(</span><span class="n">new_state</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</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;Sets the random number generator state of the specified GPU.</span>
 
 <span class="sd">    Args:</span>
@@ -467,7 +467,7 @@ <h1>Source code for torch.cuda.random</h1><div class="highlight"><pre>
         <span class="n">default_generator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">default_generators</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
         <span class="n">default_generator</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">new_state_copy</span><span class="p">)</span>
 
-    <span class="n">_lazy_call</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span>
+    <span class="n">_lazy_call</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span></div>
 
 
 <span class="k">def</span> <span class="nf">set_rng_state_all</span><span class="p">(</span><span class="n">new_states</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -519,7 +519,7 @@ <h1>Source code for torch.cuda.random</h1><div class="highlight"><pre>
     <span class="n">_lazy_call</span><span class="p">(</span><span class="n">cb</span><span class="p">,</span> <span class="n">seed_all</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
 
 
-<span class="k">def</span> <span class="nf">seed</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<div class="viewcode-block" id="seed"><a class="viewcode-back" href="../../../generated/torch.cuda.seed.html#torch.cuda.seed">[docs]</a><span class="k">def</span> <span class="nf">seed</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;Sets the seed for generating random numbers to a random number for the current GPU.</span>
 <span class="sd">    It&#39;s safe to call this function if CUDA is not available; in that</span>
 <span class="sd">    case, it is silently ignored.</span>
@@ -533,10 +533,10 @@ <h1>Source code for torch.cuda.random</h1><div class="highlight"><pre>
         <span class="n">default_generator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">default_generators</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
         <span class="n">default_generator</span><span class="o">.</span><span class="n">seed</span><span class="p">()</span>
 
-    <span class="n">_lazy_call</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span>
+    <span class="n">_lazy_call</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span></div>
 
 
-<span class="k">def</span> <span class="nf">seed_all</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<div class="viewcode-block" id="seed_all"><a class="viewcode-back" href="../../../generated/torch.cuda.seed_all.html#torch.cuda.seed_all">[docs]</a><span class="k">def</span> <span class="nf">seed_all</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;Sets the seed for generating random numbers to a random number on all GPUs.</span>
 <span class="sd">    It&#39;s safe to call this function if CUDA is not available; in that</span>
 <span class="sd">    case, it is silently ignored.</span>
@@ -553,7 +553,7 @@ <h1>Source code for torch.cuda.random</h1><div class="highlight"><pre>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">default_generator</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">random_seed</span><span class="p">)</span>
 
-    <span class="n">_lazy_call</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span>
+    <span class="n">_lazy_call</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="initial_seed"><a class="viewcode-back" href="../../../generated/torch.cuda.initial_seed.html#torch.cuda.initial_seed">[docs]</a><span class="k">def</span> <span class="nf">initial_seed</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>

docs/master/_modules/torch/ao/quantization/observer.html

@@ -479,7 +479,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">broadcast_tensors</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>  <span class="c1"># type: ignore[attr-defined]</span></div>
 
 
-<div class="viewcode-block" id="broadcast_shapes"><a class="viewcode-back" href="../../generated/torch.broadcast_shapes.html#torch.broadcast_shapes">[docs]</a><span class="k">def</span> <span class="nf">broadcast_shapes</span><span class="p">(</span><span class="o">*</span><span class="n">shapes</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">broadcast_shapes</span><span class="p">(</span><span class="o">*</span><span class="n">shapes</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;broadcast_shapes(*shapes) -&gt; Size</span>
 
 <span class="sd">    Similar to :func:`broadcast_tensors` but for shapes.</span>
@@ -539,7 +539,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
             <span class="n">scalar</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((),</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
             <span class="n">tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">scalar</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">shapes</span><span class="p">]</span>
             <span class="n">tensors</span> <span class="o">=</span> <span class="n">broadcast_tensors</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span></div>
+            <span class="k">return</span> <span class="n">tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
 
 
 
@@ -891,7 +891,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
                  <span class="n">indexing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">_meshgrid</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="n">indexing</span><span class="p">)</span>
 <span class="k">else</span><span class="p">:</span>
-    <span class="k">def</span> <span class="nf">meshgrid</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">,</span> <span class="n">indexing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
+<div class="viewcode-block" id="meshgrid"><a class="viewcode-back" href="../../generated/torch.meshgrid.html#torch.meshgrid">[docs]</a>    <span class="k">def</span> <span class="nf">meshgrid</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">,</span> <span class="n">indexing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
         <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.</span>
 
 <span class="sd">        This is helpful when you want to visualize data over some</span>
@@ -984,7 +984,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
 <span class="sd">            :width: 512</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">_meshgrid</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="n">indexing</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">_meshgrid</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="n">indexing</span><span class="p">)</span></div>
 
 
 <span class="k">def</span> <span class="nf">_meshgrid</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">,</span> <span class="n">indexing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]):</span>
@@ -1622,7 +1622,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">cartesian_prod</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="o">*</span><span class="n">tensors</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">cartesian_prod</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>  <span class="c1"># type: ignore[attr-defined]</span></div>
 
-<div class="viewcode-block" id="block_diag"><a class="viewcode-back" href="../../generated/torch.block_diag.html#torch.block_diag">[docs]</a><span class="k">def</span> <span class="nf">block_diag</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">block_diag</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Create a block diagonal matrix from provided tensors.</span>
 
 <span class="sd">    Args:</span>
@@ -1655,7 +1655,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
     <span class="c1"># This wrapper exists to support variadic args.</span>
     <span class="k">if</span> <span class="n">has_torch_function</span><span class="p">(</span><span class="n">tensors</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">block_diag</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="o">*</span><span class="n">tensors</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_VariableFunctions</span><span class="o">.</span><span class="n">block_diag</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>  <span class="c1"># type: ignore[attr-defined]</span></div>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_VariableFunctions</span><span class="o">.</span><span class="n">block_diag</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>  <span class="c1"># type: ignore[attr-defined]</span>
 
 
 <div class="viewcode-block" id="cdist"><a class="viewcode-back" href="../../generated/torch.cdist.html#torch.cdist">[docs]</a><span class="k">def</span> <span class="nf">cdist</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">2.</span><span class="p">,</span> <span class="n">compute_mode</span><span class="o">=</span><span class="s1">&#39;use_mm_for_euclid_dist_if_necessary&#39;</span><span class="p">):</span>