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<div class="section" id="torch-diagonal">
<h1>torch.diagonal<a class="headerlink" href="#torch-diagonal" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.diagonal">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">diagonal</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">offset=0</em>, <em class="sig-param">dim1=0</em>, <em class="sig-param">dim2=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.diagonal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a partial view of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> with the its diagonal elements
with respect to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim1</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim2</span></code> appended as a dimension
at the end of the shape.</p>
<p>The argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">offset</span></code> controls which diagonal to consider:</p>
<ul class="simple">
<li><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">offset</span></code> = 0, it is the main diagonal.</p></li>
<li><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">offset</span></code> > 0, it is above the main diagonal.</p></li>
<li><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">offset</span></code> < 0, it is below the main diagonal.</p></li>
</ul>
<p>Applying <a class="reference internal" href="torch.diag_embed.html#torch.diag_embed" title="torch.diag_embed"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.diag_embed()</span></code></a> to the output of this function with
the same arguments yields a diagonal matrix with the diagonal entries
of the input. However, <a class="reference internal" href="torch.diag_embed.html#torch.diag_embed" title="torch.diag_embed"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.diag_embed()</span></code></a> has different default
dimensions, so those need to be explicitly specified.</p>
<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>) – the input tensor. Must be at least 2-dimensional.</p></li>
<li><p><strong>offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – which diagonal to consider. Default: 0
(main diagonal).</p></li>
<li><p><strong>dim1</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – first dimension with respect to which to
take diagonal. Default: 0.</p></li>
<li><p><strong>dim2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – second dimension with respect to which to
take diagonal. Default: 1.</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To take a batch diagonal, pass in dim1=-2, dim2=-1.</p>
</div>
<p>Examples:</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">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[-1.0854, 1.1431, -0.1752],</span>
<span class="go"> [ 0.8536, -0.0905, 0.0360],</span>
<span class="go"> [ 0.6927, -0.3735, -0.4945]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">tensor([-1.0854, -0.0905, -0.4945])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="go">tensor([ 1.1431, 0.0360])</span>
<span class="gp">>>> </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">5</span><span class="p">,</span> <span class="mi">4</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">diagonal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">offset</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim1</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim2</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="go">tensor([[[-1.2631, 0.3755, -1.5977, -1.8172],</span>
<span class="go"> [-1.1065, 1.0401, -0.2235, -0.7938]],</span>
<span class="go"> [[-1.7325, -0.3081, 0.6166, 0.2335],</span>
<span class="go"> [ 1.0500, 0.7336, -0.3836, -1.1015]]])</span>
</pre></div>
</div>
</dd></dl>
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