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<div class="section" id="torch-diagflat">
<h1>torch.diagflat<a class="headerlink" href="#torch-diagflat" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.diagflat">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">diagflat</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">offset=0</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.diagflat" 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 tensor with more than one dimension, then returns a
2-D tensor with diagonal elements equal to a flattened <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></li>
</ul>
<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>
<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>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>) – the diagonal to consider. Default: 0 (main
diagonal).</p></li>
</ul>
</dd>
</dl>
<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="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([-0.2956, -0.9068, 0.1695])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diagflat</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor([[-0.2956, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, -0.9068, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, 0.1695]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diagflat</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, -0.2956, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, -0.9068, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000, 0.1695],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000, 0.0000]])</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[ 0.2094, -0.3018],</span>
<span class="go"> [-0.1516, 1.9342]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diagflat</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor([[ 0.2094, 0.0000, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, -0.3018, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, -0.1516, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000, 1.9342]])</span>
</pre></div>
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