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<div class="section" id="torch-column-stack">
<h1>torch.column_stack<a class="headerlink" href="#torch-column-stack" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.column_stack">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">column_stack</code><span class="sig-paren">(</span><em class="sig-param">tensors</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.column_stack" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new tensor by horizontally stacking the tensors in <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensors</span></code>.</p>
<p>Equivalent to <code class="docutils literal notranslate"><span class="pre">torch.hstack(tensors)</span></code>, except each zero or one dimensional tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>
in <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensors</span></code> is first reshaped into a <code class="docutils literal notranslate"><span class="pre">(t.numel(),</span> <span class="pre">1)</span></code> column before being stacked horizontally.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensors</strong> (<em>sequence of Tensors</em>) – sequence of tensors to concatenate</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><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.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">b</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">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
<span class="go">tensor([[1, 4],</span>
<span class="go"> [2, 5],</span>
<span class="go"> [3, 6]])</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</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="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
<span class="go">tensor([[0, 0, 1, 0, 1],</span>
<span class="go"> [1, 2, 3, 2, 3],</span>
<span class="go"> [2, 4, 5, 4, 5],</span>
<span class="go"> [3, 6, 7, 6, 7],</span>
<span class="go"> [4, 8, 9, 8, 9]])</span>
</pre></div>
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