Generate Python docs from pytorch/pytorch@a3c5a80

Author: pytorchbot

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@@ -789,9 +789,17 @@ <h1>Source code for torch.ao.quantization.backend_config.backend_config</h1><div
 
 <div class="viewcode-block" id="BackendPatternConfig.set_observation_type"><a class="viewcode-back" href="../../../../../generated/torch.ao.quantization.backend_config.BackendPatternConfig.html#torch.ao.quantization.backend_config.BackendPatternConfig.set_observation_type">[docs]</a>    <span class="k">def</span> <span class="nf">set_observation_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">observation_type</span><span class="p">:</span> <span class="n">ObservationType</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">BackendPatternConfig</span><span class="p">:</span>
         <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Set how observers should be inserted for this pattern.</span>
-<span class="sd">        See :class:`~torch.ao.quantization.backend_config.ObservationType` for details</span>
+<span class="sd">        Set how observers should be inserted in the graph for this pattern.</span>
+<span class="sd">        There are two observation types:</span>
 
+<span class="sd">            `OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT` (default): the output observer instance will be</span>
+<span class="sd">            different from the input. This is the most common observation type.</span>
+
+<span class="sd">            `OUTPUT_SHARE_OBSERVER_WITH_INPUT`: the output observer instance will be the same as the input.</span>
+<span class="sd">            This is useful for operators like `cat`.</span>
+
+<span class="sd">        Note: This will be renamed in the near future, since we will soon insert QuantDeQuantStubs with</span>
+<span class="sd">        observers (and fake quantizes) attached instead of observers themselves.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">observation_type</span> <span class="o">=</span> <span class="n">observation_type</span>
         <span class="k">return</span> <span class="bp">self</span></div>
@@ -843,6 +851,11 @@ <h1>Source code for torch.ao.quantization.backend_config.backend_config</h1><div
 <div class="viewcode-block" id="BackendPatternConfig.set_fuser_method"><a class="viewcode-back" href="../../../../../generated/torch.ao.quantization.backend_config.BackendPatternConfig.html#torch.ao.quantization.backend_config.BackendPatternConfig.set_fuser_method">[docs]</a>    <span class="k">def</span> <span class="nf">set_fuser_method</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fuser_method</span><span class="p">:</span> <span class="n">Callable</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">BackendPatternConfig</span><span class="p">:</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Set the function that specifies how to fuse the pattern for this pattern.</span>
+
+<span class="sd">        The first argument of this function should be `is_qat`, and the rest of the arguments</span>
+<span class="sd">        should be the items in the tuple pattern, e.g. (`torch.nn.ReLU`, `torch.nn.Linear`)</span>
+<span class="sd">        will have a function with three arguments, `is_qat`, `relu`, and `linear`.</span>
+<span class="sd">        The return value of this function should be the resulting fused module.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fuser_method</span> <span class="o">=</span> <span class="n">fuser_method</span>
         <span class="k">return</span> <span class="bp">self</span></div>

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