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<div class="section" id="torch-searchsorted">
<h1>torch.searchsorted<a class="headerlink" href="#torch-searchsorted" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.searchsorted">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">searchsorted</code><span class="sig-paren">(</span><em class="sig-param">sorted_sequence</em>, <em class="sig-param">values</em>, <em class="sig-param">*</em>, <em class="sig-param">out_int32=False</em>, <em class="sig-param">right=False</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.searchsorted" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the indices from the <em>innermost</em> dimension of <code class="xref py py-attr docutils literal notranslate"><span class="pre">sorted_sequence</span></code> such that, if the
corresponding values in <code class="xref py py-attr docutils literal notranslate"><span class="pre">values</span></code> were inserted before the indices, the order of the
corresponding <em>innermost</em> dimension within <code class="xref py py-attr docutils literal notranslate"><span class="pre">sorted_sequence</span></code> would be preserved.
Return a new tensor with the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">values</span></code>. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">right</span></code> is False (default),
then the left boundary of <code class="xref py py-attr docutils literal notranslate"><span class="pre">sorted_sequence</span></code> is closed. More formally, the returned index
satisfies the following rules:</p>
<table class="colwidths-given docutils colwidths-auto align-default">
<colgroup>
<col style="width: 12%" />
<col style="width: 10%" />
<col style="width: 78%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">sorted_sequence</span></code></p></th>
<th class="head"><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">right</span></code></p></th>
<th class="head"><p><em>returned index satisfies</em></p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>1-D</p></td>
<td><p>False</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">sorted_sequence[i-1]</span> <span class="pre"><</span> <span class="pre">values[m][n]...[l][x]</span> <span class="pre"><=</span> <span class="pre">sorted_sequence[i]</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>1-D</p></td>
<td><p>True</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">sorted_sequence[i-1]</span> <span class="pre"><=</span> <span class="pre">values[m][n]...[l][x]</span> <span class="pre"><</span> <span class="pre">sorted_sequence[i]</span></code></p></td>
</tr>
<tr class="row-even"><td><p>N-D</p></td>
<td><p>False</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">sorted_sequence[m][n]...[l][i-1]</span> <span class="pre"><=</span> <span class="pre">values[m][n]...[l][x]</span> <span class="pre"><</span> <span class="pre">sorted_sequence[m][n]...[l][i]</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>N-D</p></td>
<td><p>True</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">sorted_sequence[m][n]...[l][i-1]</span> <span class="pre"><</span> <span class="pre">values[m][n]...[l][x]</span> <span class="pre"><=</span> <span class="pre">sorted_sequence[m][n]...[l][i]</span></code></p></td>
</tr>
</tbody>
</table>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>sorted_sequence</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – N-D or 1-D tensor, containing monotonically increasing sequence on the <em>innermost</em>
dimension.</p></li>
<li><p><strong>values</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><em>Scalar</em>) – N-D tensor or a Scalar containing the search value(s).</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>out_int32</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – indicate the output data type. torch.int32 if True, torch.int64 otherwise.
Default value is False, i.e. default output data type is torch.int64.</p></li>
<li><p><strong>right</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – if False, return the first suitable location that is found. If True, return the
last such index. If no suitable index found, return 0 for non-numerical value
(eg. nan, inf) or the size of <em>innermost</em> dimension within <code class="xref py py-attr docutils literal notranslate"><span class="pre">sorted_sequence</span></code>
(one pass the last index of the <em>innermost</em> dimension). In other words, if False,
gets the lower bound index for each value in <code class="xref py py-attr docutils literal notranslate"><span class="pre">values</span></code> on the corresponding
<em>innermost</em> dimension of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">sorted_sequence</span></code>. If True, gets the upper
bound index instead. Default value is False.</p></li>
<li><p><strong>out</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor, must be the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">values</span></code> if provided.</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If your use case is always 1-D sorted sequence, <a class="reference internal" href="torch.bucketize.html#torch.bucketize" title="torch.bucketize"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bucketize()</span></code></a> is preferred,
because it has fewer dimension checks resulting in slightly better performance.</p>
</div>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sorted_sequence</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">10</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">sorted_sequence</span>
<span class="go">tensor([[ 1, 3, 5, 7, 9],</span>
<span class="go"> [ 2, 4, 6, 8, 10]])</span>
<span class="gp">>>> </span><span class="n">values</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">values</span>
<span class="go">tensor([[3, 6, 9],</span>
<span class="go"> [3, 6, 9]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">sorted_sequence</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span>
<span class="go">tensor([[1, 3, 4],</span>
<span class="go"> [1, 2, 4]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">sorted_sequence</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">tensor([[2, 3, 5],</span>
<span class="go"> [1, 3, 4]])</span>
<span class="gp">>>> </span><span class="n">sorted_sequence_1d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">sorted_sequence_1d</span>
<span class="go">tensor([1, 3, 5, 7, 9])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">sorted_sequence_1d</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span>
<span class="go">tensor([[1, 3, 4],</span>
<span class="go"> [1, 3, 4]])</span>
</pre></div>
</div>
</dd></dl>
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