From 2748888ce640b16d755a257693d3814d215cd162 Mon Sep 17 00:00:00 2001
From: Gard Spreemann <gspr@nonempty.org>
Date: Tue, 16 Jul 2024 16:07:10 +0200
Subject: [PATCH] Use raw strings with TeX

Some instances of docstrings using TeX were incorrectly specified as
ordinary strings, leading to the TeX macros being interpreted as
escape sequences.
---
 ot/gnn/_layers.py       |  4 ++--
 ot/gromov/_quantized.py | 10 +++++-----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/ot/gnn/_layers.py b/ot/gnn/_layers.py
index 2326609a9..6fddc8254 100644
--- a/ot/gnn/_layers.py
+++ b/ot/gnn/_layers.py
@@ -59,7 +59,7 @@ class TFGWPooling(nn.Module):
     """
 
     def __init__(self, n_features, n_tplt=2, n_tplt_nodes=2, alpha=None, train_node_weights=True, multi_alpha=False, feature_init_mean=0., feature_init_std=1.):
-        """
+        r"""
         Template Fused Gromov-Wasserstein (TFGW) layer. This layer is a pooling layer for graph neural networks.
             Computes the fused Gromov-Wasserstein distances between the graph and a set of templates.
 
@@ -186,7 +186,7 @@ class TWPooling(nn.Module):
     """
 
     def __init__(self, n_features, n_tplt=2, n_tplt_nodes=2, train_node_weights=True, feature_init_mean=0., feature_init_std=1.):
-        """
+        r"""
         Template Wasserstein (TW) layer, also kown as OT-GNN layer. This layer is a pooling layer for graph neural networks.
             Computes the Wasserstein distances between the features of the graph features and a set of templates.
 
diff --git a/ot/gromov/_quantized.py b/ot/gromov/_quantized.py
index 147f4b221..4b952a965 100644
--- a/ot/gromov/_quantized.py
+++ b/ot/gromov/_quantized.py
@@ -230,7 +230,7 @@ def quantized_fused_gromov_wasserstein_partitioned(
 
 def get_graph_partition(C, npart, part_method='random', F=None, alpha=1.,
                         random_state=0, nx=None):
-    """
+    r"""
     Partitioning a given graph with structure matrix :math:`\mathbf{C} \in R^{n \times n}`
     into `npart` partitions either 'random', or using one of {'louvain', 'fluid'}
     algorithms from networkx, or 'spectral' clustering from scikit-learn,
@@ -335,7 +335,7 @@ def get_graph_partition(C, npart, part_method='random', F=None, alpha=1.,
 
 
 def get_graph_representants(C, part, rep_method='pagerank', random_state=0, nx=None):
-    """
+    r"""
     Get representative node for each partition given by :math:`\mathbf{part} \in R^{n}`
     of a graph with structure matrix :math:`\mathbf{C} \in R^{n \times n}`.
     Selection is either done randomly or using 'pagerank' algorithm from networkx.
@@ -408,7 +408,7 @@ def get_graph_representants(C, part, rep_method='pagerank', random_state=0, nx=N
 
 def format_partitioned_graph(C, p, part, rep_indices, F=None, M=None,
                              alpha=1., nx=None):
-    """
+    r"""
     Format an attributed graph :math:`(\mathbf{C}, \mathbf{F}, \mathbf{p})`
     with structure matrix :math:`(\mathbf{C} \in R^{n \times n}`, feature matrix
     :math:`(\mathbf{F} \in R^{n \times d}` and node relative importance
@@ -761,7 +761,7 @@ def quantized_fused_gromov_wasserstein(
 
 def get_partition_and_representants_samples(
         X, npart, method='kmeans', random_state=0, nx=None):
-    """
+    r"""
     Compute `npart` partitions and representants over samples :math:`\mathbf{X} \in R^{n \times d}`
     using either a random or a kmeans algorithm.
 
@@ -854,7 +854,7 @@ def get_partition_and_representants_samples(
 
 def format_partitioned_samples(
         X, p, part, rep_indices, F=None, alpha=1., nx=None):
-    """
+    r"""
     Format an attributed graph :math:`(\mathbf{D}(\mathbf{X}), \mathbf{F}, \mathbf{p})`
     with euclidean structure matrix :math:`(\mathbf{D}(\mathbf{X}) \in R^{n \times n}`,
     feature matrix :math:`(\mathbf{F} \in R^{n \times d}` and node relative importance