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<li class="toctree-l3"><a class="reference internal" href="#ot.gnn.FGW_distance_to_templates"><code class="docutils literal notranslate"><span class="pre">FGW_distance_to_templates()</span></code></a></li>
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<section id="module-ot.gnn">
<span id="ot-gnn"></span><h1>ot.gnn<a class="headerlink" href="#module-ot.gnn" title="Link to this heading"></a></h1>
<p>Layers and functions for optimal transport in Graph Neural Networks.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Note that by default the module is not imported in <a class="reference internal" href="../all.html#module-ot" title="ot"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ot</span></code></a>. In order to
use it you need to explicitly import <a class="reference internal" href="#module-ot.gnn" title="ot.gnn"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ot.gnn</span></code></a>. This module is PyTorch Geometric dependent.
The layers are compatible with their API.</p>
</div>
<dl class="py function">
<dt class="sig sig-object py" id="ot.gnn.FGW_distance_to_templates">
<span class="sig-prename descclassname"><span class="pre">ot.gnn.</span></span><span class="sig-name descname"><span class="pre">FGW_distance_to_templates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">G_edges</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tplt_adjacencies</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">G_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tplt_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tplt_weights</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_alpha</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">batch</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 internal" href="../_modules/ot/gnn/_utils.html#FGW_distance_to_templates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ot.gnn.FGW_distance_to_templates" title="Link to this definition"></a></dt>
<dd><p>Computes the FGW distances between a graph and templates.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>G_edges</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>shape</em><em> (</em><em>n_edges</em><em>, </em><em>2</em><em>)</em>) – Edge indices of the graph in the Pytorch Geometric format.</p></li>
<li><p><strong>tplt_adjacencies</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>shape</em><em> (</em><em>n_templates</em><em>, </em><em>n_template_nodes</em><em>, </em><em>n_templates_nodes</em><em>)</em>) – List of the adjacency matrices of the templates.</p></li>
<li><p><strong>G_features</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>shape</em><em> (</em><em>n_nodes</em><em>, </em><em>n_features</em><em>)</em>) – Graph node features.</p></li>
<li><p><strong>tplt_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>shape</em><em> (</em><em>n_templates</em><em>, </em><em>n_template_nodes</em><em>, </em><em>n_features</em><em>)</em>) – List of the node features of the templates.</p></li>
<li><p><strong>weights</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>shape</em><em> (</em><em>n_templates</em><em>, </em><em>n_template_nodes</em><em>)</em>) – Weights on the nodes of the templates.</p></li>
<li><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em>, </em><em>optional</em>) – Trade-off parameter (0 < alpha < 1).
Weights features (alpha=0) and structure (alpha=1).</p></li>
<li><p><strong>multi_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, the alpha parameter is a vector of size n_templates.</p></li>
<li><p><strong>batch</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Batch vector which assigns each node to its graph.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>distances</strong> – Vector of fused Gromov-Wasserstein distances between the graph and the templates.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)">torch.Tensor</a>, shape (n_templates) if batch=None, else shape (n_graphs, n_templates).</p>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="ot.gnn.TFGWPooling">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ot.gnn.</span></span><span class="sig-name descname"><span class="pre">TFGWPooling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_tplt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_tplt_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</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">train_node_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_alpha</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">feature_init_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_init_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/ot/gnn/_layers.html#TFGWPooling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ot.gnn.TFGWPooling" title="Link to this definition"></a></dt>
<dd><dl class="simple">
<dt>Template Fused Gromov-Wasserstein (TFGW) layer. This layer is a pooling layer for graph neural networks.</dt><dd><p>Computes the fused Gromov-Wasserstein distances between the graph and a set of templates.</p>
</dd>
</dl>
<div class="math notranslate nohighlight">
\[TFGW_{ \overline{ \mathcal{G} }, \alpha }(C,F,h)=[ FGW_{\alpha}(C,F,h,\overline{C}_k,\overline{F}_k,\overline{h}_k)]_{k=1}^{K}\]</div>
<p>where :</p>
<ul class="simple">
<li><p><span class="math notranslate nohighlight">\(\mathcal{G}=\{(\overline{C}_k,\overline{F}_k,\overline{h}_k) \}_{k \in \{1,...,K \}} \}\)</span> is the set of <span class="math notranslate nohighlight">\(K\)</span> templates characterized by their adjacency matrices <span class="math notranslate nohighlight">\(\overline{C}_k\)</span>, their feature matrices <span class="math notranslate nohighlight">\(\overline{F}_k\)</span> and their node weights <span class="math notranslate nohighlight">\(\overline{h}_k\)</span>.</p></li>
<li><p><span class="math notranslate nohighlight">\(C\)</span>, <span class="math notranslate nohighlight">\(F\)</span> and <span class="math notranslate nohighlight">\(h\)</span> are respectively the adjacency matrix, the feature matrix and the node weights of the graph.</p></li>
<li><p><span class="math notranslate nohighlight">\(\alpha\)</span> is the trade-off parameter between features and structure for the Fused Gromov-Wasserstein distance.</p></li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>n_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – Feature dimension of the nodes.</p></li>
<li><p><strong>n_tplt</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – Number of graph templates.</p></li>
<li><p><strong>n_tplt_nodes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – Number of nodes in each template.</p></li>
<li><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em>, </em><em>optional</em>) – FGW trade-off parameter (0 < alpha < 1). If None alpha is trained, else it is fixed at the given value.
Weights features (alpha=0) and structure (alpha=1).</p></li>
<li><p><strong>train_node_weights</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, the templates node weights are learned.
Else, they are uniform.</p></li>
<li><p><strong>multi_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, the alpha parameter is a vector of size n_tplt.</p></li>
<li><p><strong>feature_init_mean</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em>, </em><em>optional</em>) – Mean of the random normal law to initialize the template features.</p></li>
<li><p><strong>feature_init_std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em>, </em><em>optional</em>) – Standard deviation of the random normal law to initialize the template features.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">References</p>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id1" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>53<span class="fn-bracket">]</span></span>
<p>Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty.
“Template based graph neural network with optimal transport distances”</p>
</aside>
</aside>
<dl class="py method">
<dt class="sig sig-object py" id="ot.gnn.TFGWPooling.forward">
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch</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 internal" href="../_modules/ot/gnn/_layers.html#TFGWPooling.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ot.gnn.TFGWPooling.forward" title="Link to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a>) – Node features.</p></li>
<li><p><strong>edge_index</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a>) – Edge indices.</p></li>
<li><p><strong>batch</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Batch vector which assigns each node to its graph.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="ot.gnn.TWPooling">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ot.gnn.</span></span><span class="sig-name descname"><span class="pre">TWPooling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_tplt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_tplt_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_node_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_init_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_init_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/ot/gnn/_layers.html#TWPooling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ot.gnn.TWPooling" title="Link to this definition"></a></dt>
<dd><dl class="simple">
<dt>Template Wasserstein (TW) layer, also known as OT-GNN layer. This layer is a pooling layer for graph neural networks.</dt><dd><p>Computes the Wasserstein distances between the features of the graph features and a set of templates.</p>
</dd>
</dl>
<div class="math notranslate nohighlight">
\[TW_{\overline{\mathcal{G}}}(C,F,h)=[W(F,h,\overline{F}_k,\overline{h}_k)]_{k=1}^{K}\]</div>
<p>where :</p>
<ul class="simple">
<li><p><span class="math notranslate nohighlight">\(\mathcal{G}=\{(\overline{F}_k,\overline{h}_k) \}_{k \in \{1,...,K \}} \}\)</span> is the set of <span class="math notranslate nohighlight">\(K\)</span> templates characterized by their feature matrices <span class="math notranslate nohighlight">\(\overline{F}_k\)</span> and their node weights <span class="math notranslate nohighlight">\(\overline{h}_k\)</span>.</p></li>
<li><p><span class="math notranslate nohighlight">\(F\)</span> and <span class="math notranslate nohighlight">\(h\)</span> are respectively the feature matrix and the node weights of the graph.</p></li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>n_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – Feature dimension of the nodes.</p></li>
<li><p><strong>n_tplt</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – Number of graph templates.</p></li>
<li><p><strong>n_tplt_nodes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – Number of nodes in each template.</p></li>
<li><p><strong>train_node_weights</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, the templates node weights are learned.
Else, they are uniform.</p></li>
<li><p><strong>feature_init_mean</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em>, </em><em>optional</em>) – Mean of the random normal law to initialize the template features.</p></li>
<li><p><strong>feature_init_std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em>, </em><em>optional</em>) – Standard deviation of the random normal law to initialize the template features.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">References</p>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id2" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>54<span class="fn-bracket">]</span></span>
<p>Bécigneul, G., Ganea, O. E., Chen, B., Barzilay, R., & Jaakkola, T. S. (2020). [Optimal transport graph neural networks]</p>
</aside>
</aside>
<dl class="py method">
<dt class="sig sig-object py" id="ot.gnn.TWPooling.forward">
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_index</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">batch</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 internal" href="../_modules/ot/gnn/_layers.html#TWPooling.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ot.gnn.TWPooling.forward" title="Link to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a>) – Node features.</p></li>
<li><p><strong>edge_index</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a>) – Edge indices.</p></li>
<li><p><strong>batch</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Batch vector which assigns each node to its graph.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="ot.gnn.wasserstein_distance_to_templates">
<span class="sig-prename descclassname"><span class="pre">ot.gnn.</span></span><span class="sig-name descname"><span class="pre">wasserstein_distance_to_templates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">G_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tplt_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tplt_weights</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch</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 internal" href="../_modules/ot/gnn/_utils.html#wasserstein_distance_to_templates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ot.gnn.wasserstein_distance_to_templates" title="Link to this definition"></a></dt>
<dd><p>Computes the Wasserstein distances between a graph and graph templates.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>G_features</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>shape</em><em> (</em><em>n_nodes</em><em>, </em><em>n_features</em><em>)</em>) – Node features of the graph.</p></li>
<li><p><strong>tplt_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>shape</em><em> (</em><em>n_templates</em><em>, </em><em>n_template_nodes</em><em>, </em><em>n_features</em><em>)</em>) – List of the node features of the templates.</p></li>
<li><p><strong>weights</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>shape</em><em> (</em><em>n_templates</em><em>, </em><em>n_template_nodes</em><em>)</em>) – Weights on the nodes of the templates.</p></li>
<li><p><strong>batch</strong> (<a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Batch vector which assigns each node to its graph.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>distances</strong> – Vector of Wasserstein distances between the graph and the templates.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.8)">torch.Tensor</a>, shape (n_templates) if batch=None, else shape (n_graphs, n_templates)</p>
</dd>
</dl>
</dd></dl>
</section>
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