deploy: SimplexLab/TorchJD@b6880cd

Author: ValerianRey

latest/docs/autojac/jac/index.html

@@ -295,7 +295,7 @@
 <h1>jac<a class="headerlink" href="#jac" title="Link to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torchjd.autojac.jac">
-<span class="sig-prename descclassname"><span class="pre">torchjd.autojac.</span></span><span class="sig-name descname"><span class="pre">jac</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</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="n"><span class="pre">parallel_chunk_size</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 external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/autojac/_jac.py#L18-L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.autojac.jac" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torchjd.autojac.</span></span><span class="sig-name descname"><span class="pre">jac</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</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="n"><span class="pre">parallel_chunk_size</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 external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/autojac/_jac.py#L17-L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.autojac.jac" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the Jacobian of all values in <code class="docutils literal notranslate"><span class="pre">outputs</span></code> with respect to all <code class="docutils literal notranslate"><span class="pre">inputs</span></code>. Returns the
 result as a tuple, with one Jacobian per input tensor. The returned Jacobian with respect to
 input <code class="docutils literal notranslate"><span class="pre">t</span></code> has shape <code class="docutils literal notranslate"><span class="pre">[m]</span> <span class="pre">+</span> <span class="pre">t.shape</span></code>.</p>
@@ -304,7 +304,7 @@ <h1>jac<a class="headerlink" href="#jac" title="Link to this heading">¶</a></h1
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>outputs</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.14)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code></a>[<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a>] | <a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a></span>) – The tensor or tensors to differentiate. Should be non-empty. The Jacobians will
 have one row for each value of each of these tensors.</p></li>
-<li><p><strong>inputs</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.14)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Iterable</span></code></a>[<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a>] | <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></span>) – The tensors with respect to which the Jacobian must be computed. These must have
+<li><p><strong>inputs</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterable" title="(in Python v3.14)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Iterable</span></code></a>[<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a>] | <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></span>) – The tensors with respect to which the Jacobian must be computed. These must have
 their <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> flag set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. If not provided, defaults to the leaf tensors
 that were used to compute the <code class="docutils literal notranslate"><span class="pre">outputs</span></code> parameter.</p></li>
 <li><p><strong>retain_graph</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.14)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></span>) – If <code class="docutils literal notranslate"><span class="pre">False</span></code>, the graph used to compute the grad will be freed. Defaults to