deploy: SimplexLab/TorchJD@3fb2e03

Author: ValerianRey

latest/docs/autojac/backward/index.html

@@ -295,7 +295,7 @@
 <h1>backward<a class="headerlink" href="#backward" title="Link to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torchjd.autojac.backward">
-<span class="sig-prename descclassname"><span class="pre">torchjd.autojac.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">jac_tensors</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">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/SimplexLab/TorchJD/blob/main/src/torchjd/autojac/_backward.py#L18-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.autojac.backward" 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">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="positional-only-separator o"><abbr title="Positional-only parameter separator (PEP 570)"><span class="pre">/</span></abbr></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">jac_tensors</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">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/SimplexLab/TorchJD/blob/main/src/torchjd/autojac/_backward.py#L14-L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.autojac.backward" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the Jacobians of <code class="docutils literal notranslate"><span class="pre">tensors</span></code> with respect to <code class="docutils literal notranslate"><span class="pre">inputs</span></code>, left-multiplied by
 <code class="docutils literal notranslate"><span class="pre">jac_tensors</span></code> (or identity if <code class="docutils literal notranslate"><span class="pre">jac_tensors</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>), and accumulates the results in the
 <code class="docutils literal notranslate"><span class="pre">.jac</span></code> fields of the <code class="docutils literal notranslate"><span class="pre">inputs</span></code>.</p>

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></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">jac_outputs</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/SimplexLab/TorchJD/blob/main/src/torchjd/autojac/_jac.py#L21-L165"><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></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">jac_outputs</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/SimplexLab/TorchJD/blob/main/src/torchjd/autojac/_jac.py#L17-L161"><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 Jacobians of <code class="docutils literal notranslate"><span class="pre">outputs</span></code> with respect to <code class="docutils literal notranslate"><span class="pre">inputs</span></code>, left-multiplied by
 <code class="docutils literal notranslate"><span class="pre">jac_outputs</span></code> (or identity if <code class="docutils literal notranslate"><span class="pre">jac_outputs</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>), and 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