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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
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<span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
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<span class="k">def</span> <span class="nf">_call_end_callbacks_on_future</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fut</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> _call_end_callbacks_on_future is meant to be used for profiling async</span>
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<span class="sd"> calls that return a future. Calling this function will extend recording</span>
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<span class="sd"> beyond this scope, until the future is satisfied. It is useful for profiling</span>
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<span class="sd"> the end to end time of asynchronous calls. This function should only be called</span>
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<span class="sd"> times.</span>
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<span class="sd"> Arguments:</span>
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<span class="sd"> fut: (torch.distributed.rpc.Future or torch._C.Future): future for which to schedule</span>
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<span class="sd"> callback for.</span>
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<span class="c1"># futures are merged and rpc_async returns a consistent type (https://github.com/pytorch/pytorch/issues/34999).</span>
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<span class="c1"># jit Future, call jit operator</span>
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<div class="viewcode-block" id="emit_nvtx"><a class="viewcode-back" href="../../../autograd.html#torch.autograd.profiler.emit_nvtx">[docs]</a><span class="k">class</span> <span class="nc">emit_nvtx</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>

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