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<div class="section" id="torch-diag">
<h1>torch.diag<a class="headerlink" href="#torch-diag" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.diag">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">diag</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">diagonal=0</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.diag" title="Permalink to this definition">¶</a></dt>
<dd><ul class="simple">
<li><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is a vector (1-D tensor), then returns a 2-D square tensor
with the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> as the diagonal.</p></li>
<li><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is a matrix (2-D tensor), then returns a 1-D tensor with
the diagonal elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></li>
</ul>
<p>The argument <a class="reference internal" href="torch.diagonal.html#torch.diagonal" title="torch.diagonal"><code class="xref py py-attr docutils literal notranslate"><span class="pre">diagonal</span></code></a> controls which diagonal to consider:</p>
<ul class="simple">
<li><p>If <a class="reference internal" href="torch.diagonal.html#torch.diagonal" title="torch.diagonal"><code class="xref py py-attr docutils literal notranslate"><span class="pre">diagonal</span></code></a> = 0, it is the main diagonal.</p></li>
<li><p>If <a class="reference internal" href="torch.diagonal.html#torch.diagonal" title="torch.diagonal"><code class="xref py py-attr docutils literal notranslate"><span class="pre">diagonal</span></code></a> > 0, it is above the main diagonal.</p></li>
<li><p>If <a class="reference internal" href="torch.diagonal.html#torch.diagonal" title="torch.diagonal"><code class="xref py py-attr docutils literal notranslate"><span class="pre">diagonal</span></code></a> < 0, it is below the main diagonal.</p></li>
</ul>
<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.</p></li>
<li><p><strong>diagonal</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>) – the diagonal to consider</p></li>
</ul>
</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>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference internal" href="torch.diagonal.html#torch.diagonal" title="torch.diagonal"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.diagonal()</span></code></a> always returns the diagonal of its input.</p>
<p><a class="reference internal" href="torch.diagflat.html#torch.diagflat" title="torch.diagflat"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.diagflat()</span></code></a> always constructs a tensor with diagonal elements
specified by the input.</p>
</div>
<p>Examples:</p>
<p>Get the square matrix where the input vector is the diagonal:</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="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([ 0.5950,-0.0872, 2.3298])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor([[ 0.5950, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000,-0.0872, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, 2.3298]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diag</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([[ 0.0000, 0.5950, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000,-0.0872, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000, 2.3298],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000, 0.0000]])</span>
</pre></div>
</div>
<p>Get the k-th diagonal of a given matrix:</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([[-0.4264, 0.0255,-0.1064],</span>
<span class="go"> [ 0.8795,-0.2429, 0.1374],</span>
<span class="go"> [ 0.1029,-0.6482,-1.6300]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diag</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([-0.4264,-0.2429,-1.6300])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diag</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([ 0.0255, 0.1374])</span>
</pre></div>
</div>
</dd></dl>
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