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<div class="section" id="torch-tensor-fill-diagonal">
<h1>torch.Tensor.fill_diagonal_<a class="headerlink" href="#torch-tensor-fill-diagonal" title="Permalink to this headline">¶</a></h1>
<dl class="py method">
<dt id="torch.Tensor.fill_diagonal_">
<code class="sig-prename descclassname"><span class="pre">Tensor.</span></code><code class="sig-name descname"><span class="pre">fill_diagonal_</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fill_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><a class="headerlink" href="#torch.Tensor.fill_diagonal_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the main diagonal of a tensor that has at least 2-dimensions.
When dims>2, all dimensions of input must be of equal length.
This function modifies the input tensor in-place, and returns the input tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>fill_value</strong> (<em>Scalar</em>) – the fill value</p></li>
<li><p><strong>wrap</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – the diagonal ‘wrapped’ after N columns for tall matrices.</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">zeros</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="o">.</span><span class="n">fill_diagonal_</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([[5., 0., 0.],</span>
<span class="go"> [0., 5., 0.],</span>
<span class="go"> [0., 0., 5.]])</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span><span class="o">.</span><span class="n">fill_diagonal_</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([[5., 0., 0.],</span>
<span class="go"> [0., 5., 0.],</span>
<span class="go"> [0., 0., 5.],</span>
<span class="go"> [0., 0., 0.],</span>
<span class="go"> [0., 0., 0.],</span>
<span class="go"> [0., 0., 0.],</span>
<span class="go"> [0., 0., 0.]])</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span><span class="o">.</span><span class="n">fill_diagonal_</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">wrap</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">tensor([[5., 0., 0.],</span>
<span class="go"> [0., 5., 0.],</span>
<span class="go"> [0., 0., 5.],</span>
<span class="go"> [0., 0., 0.],</span>
<span class="go"> [5., 0., 0.],</span>
<span class="go"> [0., 5., 0.],</span>
<span class="go"> [0., 0., 5.]])</span>
</pre></div>
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