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<div class="section" id="torch-bucketize">
<h1>torch.bucketize<a class="headerlink" href="#torch-bucketize" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.bucketize">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">bucketize</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">boundaries</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.bucketize" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the indices of the buckets to which each value in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> belongs, where the
boundaries of the buckets are set by <code class="xref py py-attr docutils literal notranslate"><span class="pre">boundaries</span></code>. Return a new tensor with the same size
as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</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 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: 15%" />
<col style="width: 85%" />
</colgroup>
<thead>
<tr class="row-odd"><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>False</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">boundaries[i-1]</span> <span class="pre"><=</span> <span class="pre">input[m][n]...[l][x]</span> <span class="pre"><</span> <span class="pre">boundaries[i]</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>True</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">boundaries[i-1]</span> <span class="pre"><</span> <span class="pre">input[m][n]...[l][x]</span> <span class="pre"><=</span> <span class="pre">boundaries[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>input</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>
<li><p><strong>boundaries</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – 1-D tensor, must contain a monotonically increasing sequence.</p></li>
<li><p><strong>out_int32</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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 v3.8)"><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 <code class="xref py py-attr docutils literal notranslate"><span class="pre">boundaries</span></code> (one pass the last index).
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">input</span></code>
from <code class="xref py py-attr docutils literal notranslate"><span class="pre">boundaries</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">input</span></code> if provided.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">boundaries</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">boundaries</span>
<span class="go">tensor([1, 3, 5, 7, 9])</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">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">v</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">bucketize</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">boundaries</span><span class="p">)</span>
<span class="go">tensor([[1, 3, 4],</span>
<span class="go"> [1, 3, 4]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">bucketize</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">boundaries</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"> [2, 3, 5]])</span>
</pre></div>
</div>
</dd></dl>
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