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  <div class="section" id="torch-dstack">
<h1>torch.dstack<a class="headerlink" href="#torch-dstack" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.dstack">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">dstack</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> &#x2192; Tensor<a class="headerlink" href="#torch.dstack" title="Permalink to this definition">¶</a></dt>
<dd><p>Stack tensors in sequence depthwise (along third axis).</p>
<p>This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by <a class="reference internal" href="torch.atleast_3d.html#torch.atleast_3d" title="torch.atleast_3d"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atleast_3d()</span></code></a>.</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>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </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">&gt;&gt;&gt; </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">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">dstack</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">&gt;&gt;&gt; </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">&gt;&gt;&gt; </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">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">dstack</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>
</pre></div>
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