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<div class="pytorch-left-menu-search">

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Expand Down Expand Up @@ -1759,7 +1759,6 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
<span class="sd"> * :func:`torch.Tensor.put_` with ``accumulate=True`` when called on a CPU</span>
<span class="sd"> tensor</span>
<span class="sd"> * :func:`torch.Tensor.scatter_add_` when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.cumsum` when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.gather` when called on a CUDA tensor that requires grad</span>
<span class="sd"> * :func:`torch.index_add` when called on CUDA tensor</span>
<span class="sd"> * :func:`torch.index_select` when attempting to differentiate a CUDA tensor</span>
Expand Down Expand Up @@ -1806,6 +1805,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
<span class="sd"> * :func:`torch.kthvalue` with called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.median` with indices output when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.nn.functional.grid_sample` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :func:`torch.cumsum` when called on a CUDA tensor when dtype is floating point or complex</span>
<span class="sd"> * :func:`torch.Tensor.scatter_reduce` when ``reduce=&#39;prod&#39;`` and called on CUDA tensor</span>
<span class="sd"> * :func:`torch.Tensor.resize_` when called with a quantized tensor</span>

Expand Down Expand Up @@ -3188,6 +3188,20 @@ <h1>Source code for torch</h1><div class="highlight"><pre>

<span class="k">if</span> <span class="n">_is_device_backend_autoload_enabled</span><span class="p">():</span>
<span class="n">_import_device_backends</span><span class="p">()</span>


<span class="k">def</span> <span class="nf">_as_tensor_fullprec</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Like torch.as_tensor, but when given Python data types it will keep</span>
<span class="sd"> them in full precision. Used for calling convention for Dynamo.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">ty</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ty</span> <span class="ow">is</span> <span class="n">builtins</span><span class="o">.</span><span class="n">float</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">ty</span> <span class="ow">is</span> <span class="n">builtins</span><span class="o">.</span><span class="n">int</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
</pre></div>

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Expand Down Expand Up @@ -1372,31 +1372,6 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>

<span class="k">return</span> <span class="n">_symeig</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eigenvectors</span><span class="o">=</span><span class="n">eigenvectors</span><span class="p">)</span>

<div class="viewcode-block" id="Tensor.cumsum"><a class="viewcode-back" href="../../generated/torch.Tensor.cumsum.html#torch.Tensor.cumsum">[docs]</a> <span class="k">def</span> <span class="nf">cumsum</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> cumsum(dim, dtype=None) -&gt; Tensor</span>

<span class="sd"> See :func:`torch.cumsum`</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">cumsum</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="n">dim</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>
<span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</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> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span></div>

<div class="viewcode-block" id="Tensor.lu"><a class="viewcode-back" href="../../generated/torch.Tensor.lu.html#torch.Tensor.lu">[docs]</a> <span class="k">def</span> <span class="nf">lu</span><span class="p">(</span><span class="bp">self</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="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;See :func:`torch.lu`&quot;&quot;&quot;</span>
<span class="c1"># If get_infos is True, then we don&#39;t need to check for errors and vice versa</span>
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