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<div class="section" id="torch-all">
<h1>torch.all<a class="headerlink" href="#torch-all" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.all">
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">all</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</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.all" title="Permalink to this definition">¶</a></dt>
<dd><p>Tests if all elements in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> evaluate to <cite>True</cite>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function matches the behaviour of NumPy in returning
output of dtype <cite>bool</cite> for all supported dtypes except <cite>uint8</cite>.
For <cite>uint8</cite> the dtype of output is <cite>uint8</cite> itself.</p>
</div>
<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">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">bool</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[False, True]], dtype=torch.bool)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor(False, dtype=torch.bool)</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">arange</span><span class="p">(</span><span class="mi">0</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, 1, 2])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor(False)</span>
</pre></div>
</div>
<dl class="py function">
<dt>
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">all</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</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></dt>
<dd></dd></dl>
<p>For each row of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> in the given dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>,
returns <cite>True</cite> if all elements in the row evaluate to <cite>True</cite> and <cite>False</cite> otherwise.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">keepdim</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, the output tensor is of the same size
as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> except in the dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> where it is of size 1.
Otherwise, <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is squeezed (see <a class="reference internal" href="torch.squeeze.html#torch.squeeze" title="torch.squeeze"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.squeeze()</span></code></a>), resulting in
the output tensor having 1 fewer dimension than <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</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.</p></li>
<li><p><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – the dimension to reduce.</p></li>
<li><p><strong>keepdim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – whether the output tensor has <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> retained or not.</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>
<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">rand</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="o">.</span><span class="n">bool</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[True, True],</span>
<span class="go"> [True, False],</span>
<span class="go"> [True, True],</span>
<span class="go"> [True, True]], dtype=torch.bool)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">tensor([ True, False, True, True], dtype=torch.bool)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">tensor([ True, False], dtype=torch.bool)</span>
</pre></div>
</div>
</dd></dl>
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