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<li class="toctree-l1"><a class="reference internal" href="../jit.html">torch.jit</a></li>
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<li><a href="torch/quasirandom.html">torch.quasirandom</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../jit.html">torch.jit</a></li>
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Expand Down Expand Up @@ -447,7 +448,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
<span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">The torch package contains data structures for multi-dimensional</span>
<span class="sd">tensors and defines mathematical operations over these tensors.</span>
<span class="sd">Additionally, it provides many utilities for efficient serializing of</span>
<span class="sd">Additionally, it provides many utilities for efficient serialization of</span>
<span class="sd">Tensors and arbitrary types, and other useful utilities.</span>

<span class="sd">It has a CUDA counterpart, that enables you to run your tensor computations</span>
Expand Down Expand Up @@ -1291,6 +1292,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">futures</span> <span class="k">as</span> <span class="n">futures</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nested</span> <span class="k">as</span> <span class="n">nested</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span> <span class="k">as</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">torch.signal</span> <span class="kn">import</span> <span class="n">windows</span> <span class="k">as</span> <span class="n">windows</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</span> <span class="k">as</span> <span class="n">optim</span>
<span class="kn">import</span> <span class="nn">torch.optim._multi_tensor</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">multiprocessing</span> <span class="k">as</span> <span class="n">multiprocessing</span>
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<li class="toctree-l1"><a class="reference internal" href="../../jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../linalg.html">torch.linalg</a></li>
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Expand Down Expand Up @@ -559,11 +560,13 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
<span class="c1"># doesn&#39;t work because of</span>
<span class="c1"># https://github.com/pytorch/pytorch/issues/47442</span>
<span class="c1"># Update the test in test_serialization if you remove &#39;meta&#39; from here</span>

<span class="k">if</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">is_sparse</span>
<span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;lazy&quot;</span><span class="p">,</span> <span class="s2">&quot;xla&quot;</span><span class="p">,</span> <span class="s2">&quot;mps&quot;</span><span class="p">,</span> <span class="s2">&quot;ort&quot;</span><span class="p">,</span> <span class="s2">&quot;meta&quot;</span><span class="p">,</span> <span class="s2">&quot;hpu&quot;</span><span class="p">]</span>
<span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">storage</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;privateuseone&quot;</span><span class="p">)</span>
<span class="ow">or</span> <span class="p">(</span>
<span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_has_storage</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;privateuseone&quot;</span>
<span class="p">)</span>
<span class="ow">or</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Tensor</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="p">):</span>
<span class="n">new_tensor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
Expand Down Expand Up @@ -720,7 +723,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
<span class="c1"># `tolist()` converts every single element in the tensor into python objects</span>
<span class="c1"># and serialize them one by one.</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;xla&quot;</span><span class="p">,</span> <span class="s2">&quot;ort&quot;</span><span class="p">,</span> <span class="s2">&quot;hpu&quot;</span><span class="p">]</span> <span class="ow">or</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">storage</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;privateuseone&quot;</span>
<span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_has_storage</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;privateuseone&quot;</span>
<span class="p">):</span>
<span class="c1"># Convert BFloat16 tesors to Float32 before conversion to numpy, as numpy doesn&#39;t</span>
<span class="c1"># support BFloat16. The rebuild tensor from numpy takes in the original self.dtype,</span>
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