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<div class="section" id="torch-jit-isinstance">
<h1>torch.jit.isinstance<a class="headerlink" href="#torch-jit-isinstance" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.jit.isinstance">
<code class="sig-prename descclassname"><span class="pre">torch.jit.</span></code><code class="sig-name descname"><span class="pre">isinstance</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#isinstance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.isinstance" title="Permalink to this definition">¶</a></dt>
<dd><p>This function provides for conatiner type refinement in TorchScript. It can refine
parameterized containers of the List, Dict, Tuple, and Optional types. E.g. <code class="docutils literal notranslate"><span class="pre">List[str]</span></code>,
<code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">List[torch.Tensor]]</span></code>, <code class="docutils literal notranslate"><span class="pre">Optional[Tuple[int,str,int]]</span></code>. It can also
refine basic types such as bools and ints that are available in TorchScript.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>obj</strong> – object to refine the type of</p></li>
<li><p><strong>target_type</strong> – type to try to refine obj to</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><dl class="simple">
<dt>True if obj was successfully refined to the type of target_type,</dt><dd><p>False otherwise with no new type refinement</p>
</dd>
</dl>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">bool</span></code></p>
</dd>
</dl>
<p>Example (using <code class="docutils literal notranslate"><span class="pre">torch.jit.isinstance</span></code> for type refinement):
.. testcode:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span> <span class="c1"># note the Any type</span>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]):</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">input</span><span class="p">:</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]):</span>
<span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">input</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">MyModule</span><span class="p">())</span>
<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</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="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">3</span><span class="p">)]</span>
<span class="n">m</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"key1"</span><span class="p">:</span><span class="s2">"val1"</span><span class="p">,</span><span class="s2">"key2"</span><span class="p">:</span><span class="s2">"val2"</span><span class="p">}</span>
<span class="n">m</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
</pre></div>
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