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  <section id="masked-select">
<h1>masked_select<a class="headerlink" href="#masked-select" title="Permalink to this headline">¶</a></h1>
<section id="documentation">
<h2>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="treetensor.torch.masked_select">
<code class="sig-prename descclassname">treetensor.torch.</code><code class="sig-name descname">masked_select</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">input</span></em>, <em class="sig-param"><span class="n">mask</span></em>, <em class="sig-param"><span class="o">*</span><span class="n">args</span></em>, <em class="sig-param"><span class="n">reduce</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/treetensor/torch/funcs/reduction.html#masked_select"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#treetensor.torch.masked_select" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new 1-D tensor which indexes the <code class="docutils literal notranslate"><span class="pre">input</span></code> tensor
according to the boolean mask <code class="docutils literal notranslate"><span class="pre">mask</span></code> which is a BoolTensor.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">treetensor.torch</span> <span class="k">as</span> <span class="nn">ttorch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">t</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="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">t</span>
<span class="go">tensor([[ 0.0481,  0.1741,  0.9820, -0.6354],</span>
<span class="go">        [ 0.8108, -0.7126,  0.1329,  1.0868],</span>
<span class="go">        [-1.8267,  1.3676, -1.4490, -2.0224]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">ttorch</span><span class="o">.</span><span class="n">masked_select</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span> <span class="o">&gt;</span> <span class="mf">0.3</span><span class="p">)</span>
<span class="go">tensor([0.9820, 0.8108, 1.0868, 1.3676])</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">tt</span> <span class="o">=</span> <span class="n">ttorch</span><span class="o">.</span><span class="n">randn</span><span class="p">({</span>
<span class="gp">... </span>    <span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
<span class="gp">... </span>    <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)},</span>
<span class="gp">... </span><span class="p">})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">tt</span>
<span class="go">&lt;Tensor 0x7f0be77bbc88&gt;</span>
<span class="go">├── a --&gt; tensor([[ 1.1799,  0.4652, -1.7895],</span>
<span class="go">│                 [ 0.0423,  1.0866,  1.3533]])</span>
<span class="go">└── b --&gt; &lt;Tensor 0x7f0be77bbb70&gt;</span>
<span class="go">    └── x --&gt; tensor([[ 0.8139, -0.6732,  0.0065,  0.9073],</span>
<span class="go">                      [ 0.0596, -2.0621, -0.1598, -1.0793],</span>
<span class="go">                      [-0.0496,  2.1392,  0.6403,  0.4041]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">ttorch</span><span class="o">.</span><span class="n">masked_select</span><span class="p">(</span><span class="n">tt</span><span class="p">,</span> <span class="n">tt</span> <span class="o">&gt;</span> <span class="mf">0.3</span><span class="p">)</span>
<span class="go">tensor([1.1799, 0.4652, 1.0866, 1.3533, 0.8139, 0.9073, 2.1392, 0.6403, 0.4041])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">ttorch</span><span class="o">.</span><span class="n">masked_select</span><span class="p">(</span><span class="n">tt</span><span class="p">,</span> <span class="n">tt</span> <span class="o">&gt;</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">reduce</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">&lt;Tensor 0x7fcb64456b38&gt;</span>
<span class="go">├── a --&gt; tensor([1.1799, 0.4652, 1.0866, 1.3533])</span>
<span class="go">└── b --&gt; &lt;Tensor 0x7fcb64456a58&gt;</span>
<span class="go">    └── x --&gt; tensor([0.8139, 0.9073, 2.1392, 0.6403, 0.4041])</span>
</pre></div>
</div>
</dd></dl>

<div class="tip admonition">
<p class="admonition-title">Torch Version Related</p>
<p>This documentation is based on
<a class="reference external" href="https://pytorch.org/docs/1.9.0+cu102/generated/torch.masked_select.html">torch.masked_select</a>
in <a class="reference external" href="https://pytorch.org/docs/1.9.0+cu102/">torch v1.9.0+cu102</a>.
<strong>Its arguments’ arrangements depend on the version of pytorch you installed</strong>.</p>
<p>If some arguments listed here are not working properly, please check your pytorch’s version
with the following command and find its documentation.</p>
<div class="highlight-shell notranslate"><div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span class="normal">1</span></pre></div></td><td class="code"><div><pre><span></span>python -c <span class="s1">&#39;import torch;print(torch.__version__)&#39;</span>
</pre></div></td></tr></table></div>
</div>
<p>The arguments and keyword arguments supported in torch v1.9.0+cu102 is listed below.</p>
</div>
</section>
<section id="description-from-torch-v1-9-0-cu102">
<h2>Description From Torch v1.9.0+cu102<a class="headerlink" href="#description-from-torch-v1-9-0-cu102" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="torch.masked_select">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">masked_select</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">input</span></em>, <em class="sig-param"><span class="n">mask</span></em>, <em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">out</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="trees.Tensor.where.auto.html#torch.Tensor" title="torch.Tensor">Tensor</a><a class="headerlink" href="#torch.masked_select" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new 1-D tensor which indexes the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor according to
the boolean mask <code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code> which is a <cite>BoolTensor</cite>.</p>
<p>The shapes of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code> tensor and the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor don’t need
to match, but they must be <span class="xref std std-ref">broadcastable</span>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The returned tensor does <strong>not</strong> use the same storage
as the original tensor</p>
</div>
<dl class="simple">
<dt>Args:</dt><dd><p>input (Tensor): the input tensor.
mask  (BoolTensor): the tensor containing the binary mask to index with</p>
</dd>
<dt>Keyword args:</dt><dd><p>out (Tensor, optional): the output tensor.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</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="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span>
<span class="go">tensor([[ 0.3552, -2.3825, -0.8297,  0.3477],</span>
<span class="go">        [-1.2035,  1.2252,  0.5002,  0.6248],</span>
<span class="go">        [ 0.1307, -2.0608,  0.1244,  2.0139]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mask</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ge</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mask</span>
<span class="go">tensor([[False, False, False, False],</span>
<span class="go">        [False, True, True, True],</span>
<span class="go">        [False, False, False, True]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">masked_select</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
<span class="go">tensor([ 1.2252,  0.5002,  0.6248,  2.0139])</span>
</pre></div>
</div>
</dd></dl>

</section>
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