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<div class="section" id="torch-diag-embed">
<h1>torch.diag_embed<a class="headerlink" href="#torch-diag-embed" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.diag_embed">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">diag_embed</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">offset=0</em>, <em class="sig-param">dim1=-2</em>, <em class="sig-param">dim2=-1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.diag_embed" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a tensor whose diagonals of certain 2D planes (specified by
<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>) are filled by <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.
To facilitate creating batched diagonal matrices, the 2D planes formed by
the last two dimensions of the returned tensor are chosen by default.</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>The size of the new matrix will be calculated to make the specified diagonal
of the size of the last input dimension.
Note that for <code class="xref py py-attr docutils literal notranslate"><span class="pre">offset</span></code> other than <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0</mn></mrow><annotation encoding="application/x-tex">0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">0</span></span></span></span>
</span>, the order of <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> matters. Exchanging them is equivalent to changing the
sign of <code class="xref py py-attr docutils literal notranslate"><span class="pre">offset</span></code>.</p>
<p>Applying <a class="reference internal" href="torch.diagonal.html#torch.diagonal" title="torch.diagonal"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.diagonal()</span></code></a> to the output of this function with
the same arguments yields a matrix identical to input. However,
<a class="reference internal" href="torch.diagonal.html#torch.diagonal" title="torch.diagonal"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.diagonal()</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 1-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 v3.8)"><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 v3.8)"><em>int</em></a><em>, </em><em>optional</em>) – first dimension with respect to which to
take diagonal. Default: -2.</p></li>
<li><p><strong>dim2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><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>
<p>Example:</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diag_embed</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor([[[ 1.5410, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, -0.2934, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, -2.1788]],</span>
<span class="go"> [[ 0.5684, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, -1.0845, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, -1.3986]]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">diag_embed</span><span class="p">(</span><span class="n">a</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">0</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([[[ 0.0000, 1.5410, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, 0.5684, 0.0000, 0.0000]],</span>
<span class="go"> [[ 0.0000, 0.0000, -0.2934, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, -1.0845, 0.0000]],</span>
<span class="go"> [[ 0.0000, 0.0000, 0.0000, -2.1788],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000, -1.3986]],</span>
<span class="go"> [[ 0.0000, 0.0000, 0.0000, 0.0000],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000, 0.0000]]])</span>
</pre></div>
</div>
</dd></dl>
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