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  <div class="section" id="torch-cat">
<h1>torch.cat<a class="headerlink" href="#torch-cat" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.cat">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">cat</code><span class="sig-paren">(</span><em class="sig-param">tensors</em>, <em class="sig-param">dim=0</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> &#x2192; Tensor<a class="headerlink" href="#torch.cat" title="Permalink to this definition">¶</a></dt>
<dd><p>Concatenates the given sequence of <code class="xref py py-attr docutils literal notranslate"><span class="pre">seq</span></code> tensors in the given dimension.
All tensors must either have the same shape (except in the concatenating
dimension) or be empty.</p>
<p><a class="reference internal" href="#torch.cat" title="torch.cat"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cat()</span></code></a> can be seen as an inverse operation for <a class="reference internal" href="torch.split.html#torch.split" title="torch.split"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.split()</span></code></a>
and <a class="reference internal" href="torch.chunk.html#torch.chunk" title="torch.chunk"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.chunk()</span></code></a>.</p>
<p><a class="reference internal" href="#torch.cat" title="torch.cat"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cat()</span></code></a> can be best understood via examples.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensors</strong> (<em>sequence of Tensors</em>) – any python sequence of tensors of the same type.
Non-empty tensors provided must have the same shape, except in the
cat dimension.</p></li>
<li><p><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em>, </em><em>optional</em>) – the dimension over which the tensors are concatenated</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.</p></li>
</ul>
</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">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">x</span>
<span class="go">tensor([[ 0.6580, -1.0969, -0.4614],</span>
<span class="go">        [-0.1034, -0.5790,  0.1497]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">tensor([[ 0.6580, -1.0969, -0.4614],</span>
<span class="go">        [-0.1034, -0.5790,  0.1497],</span>
<span class="go">        [ 0.6580, -1.0969, -0.4614],</span>
<span class="go">        [-0.1034, -0.5790,  0.1497],</span>
<span class="go">        [ 0.6580, -1.0969, -0.4614],</span>
<span class="go">        [-0.1034, -0.5790,  0.1497]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="go">tensor([[ 0.6580, -1.0969, -0.4614,  0.6580, -1.0969, -0.4614,  0.6580,</span>
<span class="go">         -1.0969, -0.4614],</span>
<span class="go">        [-0.1034, -0.5790,  0.1497, -0.1034, -0.5790,  0.1497, -0.1034,</span>
<span class="go">         -0.5790,  0.1497]])</span>
</pre></div>
</div>
</dd></dl>

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