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<h1>torch.autograd.Function.backward<a class="headerlink" href="#torch-autograd-function-backward" title="Permalink to this headline">¶</a></h1>
<dl class="py method">
<dt id="torch.autograd.Function.backward">
<em class="property"><span class="pre">static</span> </em><code class="sig-prename descclassname"><span class="pre">Function.</span></code><code class="sig-name descname"><span class="pre">backward</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#Function.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines a formula for differentiating the operation with backward mode
automatic differentiation (alias to the vjp function).</p>
<p>This function is to be overridden by all subclasses.</p>
<p>It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
as many outputs as the <a class="reference internal" href="torch.autograd.Function.forward.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> returned (None will be passed in
for non tensor outputs of the forward function),
and it should return as many tensors, as there were inputs to
<a class="reference internal" href="torch.autograd.Function.forward.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. Each argument is the gradient w.r.t the given output,
and each returned value should be the gradient w.r.t. the
corresponding input. If an input is not a Tensor or is a Tensor not
requiring grads, you can just pass None as a gradient for that input.</p>
<p>The context can be used to retrieve tensors saved during the forward
pass. It also has an attribute <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx.needs_input_grad</span></code> as a tuple
of booleans representing whether each input needs gradient. E.g.,
<a class="reference internal" href="torch.autograd.backward.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a> will have <code class="docutils literal notranslate"><span class="pre">ctx.needs_input_grad[0]</span> <span class="pre">=</span> <span class="pre">True</span></code> if the
first input to <a class="reference internal" href="torch.autograd.Function.forward.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> needs gradient computated w.r.t. the
output.</p>
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