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Generate Python docs from pytorch/pytorch@f682048 
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‎docs/1.13/_images/RReLU.png

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‎docs/1.13/_modules/torch/jit.html

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@@ -501,7 +501,7 @@ <h1>Source code for torch.jit</h1><div class="highlight"><pre>
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<span class="n">Error</span><span class="o">.</span><span class="vm">__qualname__</span> <span class="o">=</span> <span class="s2">&quot;Error&quot;</span>
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<span class="c1"># for use in python if using annotate</span>
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<div class="viewcode-block" id="annotate"><a class="viewcode-back" href="../../generated/torch.jit.annotate.html#torch.futures.annotate">[docs]</a><span class="k">def</span> <span class="nf">annotate</span><span class="p">(</span><span class="n">the_type</span><span class="p">,</span> <span class="n">the_value</span><span class="p">):</span>
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<div class="viewcode-block" id="annotate"><a class="viewcode-back" href="../../generated/torch.jit.annotate.html#torch.jit.annotate">[docs]</a><span class="k">def</span> <span class="nf">annotate</span><span class="p">(</span><span class="n">the_type</span><span class="p">,</span> <span class="n">the_value</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> This method is a pass-through function that returns `the_value`, used to hint TorchScript</span>
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<span class="sd"> compiler the type of `the_value`. It is a no-op when running outside of TorchScript.</span>
@@ -548,7 +548,7 @@ <h1>Source code for torch.jit</h1><div class="highlight"><pre>
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<span class="k">return</span> <span class="n">the_value</span></div>
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<div class="viewcode-block" id="script_if_tracing"><a class="viewcode-back" href="../../generated/torch.jit.script_if_tracing.html#torch.futures.script_if_tracing">[docs]</a><span class="k">def</span> <span class="nf">script_if_tracing</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
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<div class="viewcode-block" id="script_if_tracing"><a class="viewcode-back" href="../../generated/torch.jit.script_if_tracing.html#torch.jit.script_if_tracing">[docs]</a><span class="k">def</span> <span class="nf">script_if_tracing</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Compiles ``fn`` when it is first called during tracing. ``torch.jit.script``</span>
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<span class="sd"> has a non-negligible start up time when it is first called due to</span>
@@ -570,7 +570,7 @@ <h1>Source code for torch.jit</h1><div class="highlight"><pre>
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<span class="c1"># for torch.jit.isinstance</span>
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<div class="viewcode-block" id="isinstance"><a class="viewcode-back" href="../../generated/torch.jit.isinstance.html#torch.futures.isinstance">[docs]</a><span class="k">def</span> <span class="nf">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">target_type</span><span class="p">):</span>
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<div class="viewcode-block" id="isinstance"><a class="viewcode-back" href="../../generated/torch.jit.isinstance.html#torch.jit.isinstance">[docs]</a><span class="k">def</span> <span class="nf">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">target_type</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> This function provides for container type refinement in TorchScript. It can refine</span>
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<span class="sd"> parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``,</span>
@@ -611,7 +611,7 @@ <h1>Source code for torch.jit</h1><div class="highlight"><pre>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="k">return</span> <span class="n">_isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">target_type</span><span class="p">)</span></div>
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<div class="viewcode-block" id="strict_fusion"><a class="viewcode-back" href="../../generated/torch.jit.strict_fusion.html#torch.futures.strict_fusion">[docs]</a><span class="k">class</span> <span class="nc">strict_fusion</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
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<div class="viewcode-block" id="strict_fusion"><a class="viewcode-back" href="../../generated/torch.jit.strict_fusion.html#torch.jit.strict_fusion">[docs]</a><span class="k">class</span> <span class="nc">strict_fusion</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> This class errors if not all nodes have been fused in</span>
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<span class="sd"> inference, or symbolically differentiated in training.</span>
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<span class="k">finally</span><span class="p">:</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Graph</span><span class="o">.</span><span class="n">set_global_print_source_ranges</span><span class="p">(</span><span class="n">old_enable_source_ranges</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span>
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<div class="viewcode-block" id="enable_onednn_fusion"><a class="viewcode-back" href="../../generated/torch.jit.enable_onednn_fusion.html#torch.futures.enable_onednn_fusion">[docs]</a><span class="k">def</span> <span class="nf">enable_onednn_fusion</span><span class="p">(</span><span class="n">enabled</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
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<div class="viewcode-block" id="enable_onednn_fusion"><a class="viewcode-back" href="../../generated/torch.jit.enable_onednn_fusion.html#torch.jit.enable_onednn_fusion">[docs]</a><span class="k">def</span> <span class="nf">enable_onednn_fusion</span><span class="p">(</span><span class="n">enabled</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Enables or disables onednn JIT fusion based on the parameter `enabled`.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_jit_set_llga_enabled</span><span class="p">(</span><span class="n">enabled</span><span class="p">)</span></div>
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<div class="viewcode-block" id="onednn_fusion_enabled"><a class="viewcode-back" href="../../generated/torch.jit.onednn_fusion_enabled.html#torch.futures.onednn_fusion_enabled">[docs]</a><span class="k">def</span> <span class="nf">onednn_fusion_enabled</span><span class="p">():</span>
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<div class="viewcode-block" id="onednn_fusion_enabled"><a class="viewcode-back" href="../../generated/torch.jit.onednn_fusion_enabled.html#torch.jit.onednn_fusion_enabled">[docs]</a><span class="k">def</span> <span class="nf">onednn_fusion_enabled</span><span class="p">():</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Returns whether onednn JIT fusion is enabled</span>
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<span class="sd"> &quot;&quot;&quot;</span>

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