Update latest version of site

Author: docusaurus-bot

v/latest/api/_modules/botorch/models/robust_relevance_pursuit_model.html

@@ -84,7 +84,6 @@ <h1>Source code for botorch.models.robust_relevance_pursuit_model</h1><div class
 <span class="kn">from</span><span class="w"> </span><span class="nn">gpytorch.module</span><span class="w"> </span><span class="kn">import</span> <span class="n">Module</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">torch</span><span class="w"> </span><span class="kn">import</span> <span class="n">Tensor</span>
 
-
 <span class="c1"># default fractions of outliers to consider during relevance pursuit</span>
 <span class="n">FRACTIONS_OF_OUTLIERS</span> <span class="o">=</span> <span class="p">[</span>
     <span class="mf">0.0</span><span class="p">,</span>
@@ -142,8 +141,8 @@ <h1>Source code for botorch.models.robust_relevance_pursuit_model</h1><div class
             <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span>
             <span class="n">convex_parameterization</span><span class="o">=</span><span class="n">convex_parameterization</span><span class="p">,</span>
         <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bmc_support_size</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bmc_probability</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bmc_support_sizes</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bmc_probabilities</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">cache_model_trace</span> <span class="o">=</span> <span class="n">cache_model_trace</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">model_trace</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">SingleTaskGP</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">prior_mean_of_support</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="p">(</span>

v/latest/api/_modules/botorch/models/robust_relevance_pursuit_model/index.html

@@ -84,7 +84,6 @@ <h1>Source code for botorch.models.robust_relevance_pursuit_model</h1><div class
 <span class="kn">from</span><span class="w"> </span><span class="nn">gpytorch.module</span><span class="w"> </span><span class="kn">import</span> <span class="n">Module</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">torch</span><span class="w"> </span><span class="kn">import</span> <span class="n">Tensor</span>
 
-
 <span class="c1"># default fractions of outliers to consider during relevance pursuit</span>
 <span class="n">FRACTIONS_OF_OUTLIERS</span> <span class="o">=</span> <span class="p">[</span>
     <span class="mf">0.0</span><span class="p">,</span>
@@ -142,8 +141,8 @@ <h1>Source code for botorch.models.robust_relevance_pursuit_model</h1><div class
             <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span>
             <span class="n">convex_parameterization</span><span class="o">=</span><span class="n">convex_parameterization</span><span class="p">,</span>
         <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bmc_support_size</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bmc_probability</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bmc_support_sizes</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bmc_probabilities</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">cache_model_trace</span> <span class="o">=</span> <span class="n">cache_model_trace</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">model_trace</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">SingleTaskGP</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">prior_mean_of_support</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="p">(</span>

v/latest/api/utils.html

@@ -2369,9 +2369,9 @@
 <li><p><strong>strict</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to strictly enforce that the keys
 in <a class="reference internal" href="#botorch.utils.testing.MockModel.state_dict" title="botorch.utils.testing.MockModel.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> match the keys returned by this module’s
 <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> function. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
-<li><p><strong>assign</strong> (<em>bool</em><em>, </em><em>optional</em>) – When <code class="docutils literal notranslate"><span class="pre">False</span></code>, the properties of the tensors
-in the current module are preserved while when <code class="docutils literal notranslate"><span class="pre">True</span></code>, the
-properties of the Tensors in the state dict are preserved. The only
+<li><p><strong>assign</strong> (<em>bool</em><em>, </em><em>optional</em>) – When set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the properties of the tensors
+in the current module are preserved whereas setting it to <code class="docutils literal notranslate"><span class="pre">True</span></code> preserves
+properties of the Tensors in the state dict. The only
 exception is the <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> field of <code class="xref py py-class docutils literal notranslate">
 <span class="pre">Default:</span> <span class="pre">``False`</span></code></p></li>
 </ul>
@@ -2727,7 +2727,7 @@
 <dl class="py method">
 <dt class="sig sig-object py" id="botorch.utils.torch.BufferDict.extra_repr">
 <span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/botorch/utils/torch.html#BufferDict.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#botorch.utils.torch.BufferDict.extra_repr" title="Link to this definition">¶</a></dt>
-<dd><p>Set the extra representation of the module.</p>
+<dd><p>Return the extra representation of the module.</p>
 <p>To print customized extra information, you should re-implement
 this method in your own modules. Both single-line and multi-line
 strings are acceptable.</p>

v/latest/api/utils/index.html

@@ -2369,9 +2369,9 @@
 <li><p><strong>strict</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to strictly enforce that the keys
 in <a class="reference internal" href="#botorch.utils.testing.MockModel.state_dict" title="botorch.utils.testing.MockModel.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> match the keys returned by this module’s
 <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> function. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
-<li><p><strong>assign</strong> (<em>bool</em><em>, </em><em>optional</em>) – When <code class="docutils literal notranslate"><span class="pre">False</span></code>, the properties of the tensors
-in the current module are preserved while when <code class="docutils literal notranslate"><span class="pre">True</span></code>, the
-properties of the Tensors in the state dict are preserved. The only
+<li><p><strong>assign</strong> (<em>bool</em><em>, </em><em>optional</em>) – When set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the properties of the tensors
+in the current module are preserved whereas setting it to <code class="docutils literal notranslate"><span class="pre">True</span></code> preserves
+properties of the Tensors in the state dict. The only
 exception is the <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> field of <code class="xref py py-class docutils literal notranslate">
 <span class="pre">Default:</span> <span class="pre">``False`</span></code></p></li>
 </ul>
@@ -2727,7 +2727,7 @@
 <dl class="py method">
 <dt class="sig sig-object py" id="botorch.utils.torch.BufferDict.extra_repr">
 <span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/botorch/utils/torch.html#BufferDict.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#botorch.utils.torch.BufferDict.extra_repr" title="Link to this definition">¶</a></dt>
-<dd><p>Set the extra representation of the module.</p>
+<dd><p>Return the extra representation of the module.</p>
 <p>To print customized extra information, you should re-implement
 this method in your own modules. Both single-line and multi-line
 strings are acceptable.</p>