auto-generating sphinx docs

Author: pytorchbot

docs/master/_modules/torch/functional.html

@@ -373,7 +373,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
 <span class="p">]</span>
 
 
-<div class="viewcode-block" id="broadcast_tensors"><a class="viewcode-back" href="../../generated/torch.broadcast_tensors.html#torch.broadcast_tensors">[docs]</a><span class="k">def</span> <span class="nf">broadcast_tensors</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">broadcast_tensors</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;broadcast_tensors(*tensors) -&gt; List of Tensors</span>
 
 <span class="sd">    Broadcasts the given tensors according to :ref:`broadcasting-semantics`.</span>
@@ -402,7 +402,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
     <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
         <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Tensor</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">)</span> <span class="ow">and</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">broadcast_tensors</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">broadcast_tensors</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span></div>
+    <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>
 
 
 <div class="viewcode-block" id="split"><a class="viewcode-back" href="../../generated/torch.split.html#torch.split">[docs]</a><span class="k">def</span> <span class="nf">split</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">split_size_or_sections</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
@@ -580,7 +580,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">P</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">U</span>
 
 
-<span class="k">def</span> <span class="nf">einsum</span><span class="p">(</span><span class="n">equation</span><span class="p">,</span> <span class="o">*</span><span class="n">operands</span><span class="p">):</span>
+<div class="viewcode-block" id="einsum"><a class="viewcode-back" href="../../generated/torch.einsum.html#torch.einsum">[docs]</a><span class="k">def</span> <span class="nf">einsum</span><span class="p">(</span><span class="n">equation</span><span class="p">,</span> <span class="o">*</span><span class="n">operands</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;einsum(equation, *operands) -&gt; Tensor</span>
 
 <span class="sd">This function provides a way of computing multilinear expressions (i.e. sums of products) using the</span>
@@ -664,7 +664,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
         <span class="c1"># in the original implementation this line is omitted</span>
         <span class="k">return</span> <span class="n">einsum</span><span class="p">(</span><span class="n">equation</span><span class="p">,</span> <span class="o">*</span><span class="n">operands</span><span class="p">)</span>
 
-    <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="n">equation</span><span class="p">,</span> <span class="n">operands</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="n">equation</span><span class="p">,</span> <span class="n">operands</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>
@@ -1198,7 +1198,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
         <span class="n">dims_b</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">dims</span><span class="p">))</span>
     <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">tensordot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">dims_a</span><span class="p">,</span> <span class="n">dims_b</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="cartesian_prod"><a class="viewcode-back" href="../../generated/torch.cartesian_prod.html#torch.cartesian_prod">[docs]</a><span class="k">def</span> <span class="nf">cartesian_prod</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">cartesian_prod</span><span class="p">(</span><span class="o">*</span><span class="n">tensors</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Do cartesian product of the given sequence of tensors. The behavior is similar to</span>
 <span class="sd">    python&#39;s `itertools.product`.</span>
 
@@ -1229,9 +1229,9 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
     <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
         <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Tensor</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">)</span> <span class="ow">and</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">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></div>
+    <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>
 
-<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">    Arguments:</span>
@@ -1263,10 +1263,10 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Tensor</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">)</span> <span class="ow">and</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></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>
 
 
-<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>
+<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>
     <span class="c1"># type: (Tensor, Tensor, float, str) -&gt; (Tensor)</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Computes batched the p-norm distance between each pair of the two collections of row vectors.</span>
 
@@ -1319,7 +1319,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
     <span class="k">elif</span> <span class="n">compute_mode</span> <span class="o">==</span> <span class="s1">&#39;donot_use_mm_for_euclid_dist&#39;</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">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="p">,</span> <span class="mi">2</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> is not a valid value for compute_mode&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">compute_mode</span><span class="p">))</span></div>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> is not a valid value for compute_mode&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">compute_mode</span><span class="p">))</span>
 
 <span class="c1"># TODO: type dim as BroadcastingList when https://github.com/pytorch/pytorch/issues/33782 is fixed</span>
 <span class="nd">@overload</span>  <span class="c1"># noqa: 749</span>
@@ -1469,7 +1469,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="nb">input</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="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="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="chain_matmul"><a class="viewcode-back" href="../../generated/torch.chain_matmul.html#torch.chain_matmul">[docs]</a><span class="k">def</span> <span class="nf">chain_matmul</span><span class="p">(</span><span class="o">*</span><span class="n">matrices</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">chain_matmul</span><span class="p">(</span><span class="o">*</span><span class="n">matrices</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the matrix product of the :math:`N` 2-D tensors. This product is efficiently computed</span>
 <span class="sd">    using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms</span>
 <span class="sd">    of arithmetic operations (`[CLRS]`_). Note that since this is a function to compute the product, :math:`N`</span>
@@ -1501,7 +1501,7 @@ <h1>Source code for torch.functional</h1><div class="highlight"><pre>
     <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
         <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Tensor</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">matrices</span><span class="p">)</span> <span class="ow">and</span> <span class="n">has_torch_function</span><span class="p">(</span><span class="n">matrices</span><span class="p">):</span>
             <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">chain_matmul</span><span class="p">,</span> <span class="n">matrices</span><span class="p">,</span> <span class="o">*</span><span class="n">matrices</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">chain_matmul</span><span class="p">(</span><span class="n">matrices</span><span class="p">)</span></div>
+    <span class="k">return</span> <span class="n">_VF</span><span class="o">.</span><span class="n">chain_matmul</span><span class="p">(</span><span class="n">matrices</span><span class="p">)</span>
 
 
 <span class="k">def</span> <span class="nf">_lu_impl</span><span class="p">(</span><span class="n">A</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="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>