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<div class="section" id="torch-tensor-sparse-mask">
<h1>torch.Tensor.sparse_mask<a class="headerlink" href="#torch-tensor-sparse-mask" title="Permalink to this headline">¶</a></h1>
<dl class="py method">
<dt id="torch.Tensor.sparse_mask">
<code class="sig-prename descclassname"><span class="pre">Tensor.</span></code><code class="sig-name descname"><span class="pre">sparse_mask</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><a class="headerlink" href="#torch.Tensor.sparse_mask" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new <a class="reference internal" href="../sparse.html#sparse-docs"><span class="std std-ref">sparse tensor</span></a> with values from a
strided tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> filtered by the indices of the sparse
tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code>. The values of <code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code> sparse tensor are
ignored. <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code> tensors must have the same
shape.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The returned sparse tensor has the same indices as the sparse tensor
<code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code>, even when the corresponding values in <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> are
zeros.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>mask</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – a sparse tensor whose indices are used as a filter</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nse</span> <span class="o">=</span> <span class="mi">5</span>
<span class="gp">>>> </span><span class="n">dims</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">I</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dims</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">nse</span><span class="p">,)),</span>
<span class="gp">... </span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dims</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">nse</span><span class="p">,))],</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">nse</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">V</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">nse</span><span class="p">,</span> <span class="n">dims</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">dims</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">S</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo_tensor</span><span class="p">(</span><span class="n">I</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">dims</span><span class="p">)</span><span class="o">.</span><span class="n">coalesce</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">D</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">dims</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">D</span><span class="o">.</span><span class="n">sparse_mask</span><span class="p">(</span><span class="n">S</span><span class="p">)</span>
<span class="go">tensor(indices=tensor([[0, 0, 0, 2],</span>
<span class="go"> [0, 1, 4, 3]]),</span>
<span class="go"> values=tensor([[[ 1.6550, 0.2397],</span>
<span class="go"> [-0.1611, -0.0779]],</span>
<span class="go"> [[ 0.2326, -1.0558],</span>
<span class="go"> [ 1.4711, 1.9678]],</span>
<span class="go"> [[-0.5138, -0.0411],</span>
<span class="go"> [ 1.9417, 0.5158]],</span>
<span class="go"> [[ 0.0793, 0.0036],</span>
<span class="go"> [-0.2569, -0.1055]]]),</span>
<span class="go"> size=(5, 5, 2, 2), nnz=4, layout=torch.sparse_coo)</span>
</pre></div>
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