fix doc

3 files changed, 3 insertions, 3 deletions

diff --git a/src/python/gudhi/tensorflow/cubical_layer.py b/src/python/gudhi/tensorflow/cubical_layer.py
index e8674d7b..918ff43e 100644
--- a/src/python/gudhi/tensorflow/cubical_layer.py
+++ b/src/python/gudhi/tensorflow/cubical_layer.py
@@ -58,7 +58,7 @@ class CubicalLayer(tf.keras.layers.Layer):
             X (TensorFlow variable): pixel values of the cubical complex
 
         Returns:
-            dgms (list of tuple of TensorFlow variables): list of cubical persistence diagrams. The length of this list is the same than that of dimensions, i.e., there is one persistence diagram per homology dimension provided in the input list dimensions. Moreover, the finite and essential parts of the persistence diagrams are provided separately: each element of this list is a tuple of size two that contains the finite and essential parts of the corresponding persistence diagram, of shapes [num_finite_points, 2] and [num_essential_points, 1] respectively. Note that the essential part is always empty in cubical persistence diagrams, except in homology dimension zero, where the essential part always contains a single point, with abscissa equal to the smallest value in the complex, and infinite ordinate
+            List[Tuple[tf.Tensor,tf.Tensor]]: List of cubical persistence diagrams. The length of this list is the same than that of dimensions, i.e., there is one persistence diagram per homology dimension provided in the input list dimensions. Moreover, the finite and essential parts of the persistence diagrams are provided separately: each element of this list is a tuple of size two that contains the finite and essential parts of the corresponding persistence diagram, of shapes [num_finite_points, 2] and [num_essential_points, 1] respectively. Note that the essential part is always empty in cubical persistence diagrams, except in homology dimension zero, where the essential part always contains a single point, with abscissa equal to the smallest value in the complex, and infinite ordinate
         """
         # Compute pixels associated to positive and negative simplices 
         # Don't compute gradient for this operation
diff --git a/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py b/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py
index a2e48d8a..5606d1a4 100644
--- a/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py
+++ b/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py
@@ -65,7 +65,7 @@ class LowerStarSimplexTreeLayer(tf.keras.layers.Layer):
             F (TensorFlow variable): filter function values over the vertices of the simplex tree. The ith entry of F corresponds to vertex i in self.simplextree
 
         Returns:
-            dgms (list of tuple of TensorFlow variables): list of lower-star persistence diagrams. The length of this list is the same than that of dimensions, i.e., there is one persistence diagram per homology dimension provided in the input list dimensions. Moreover, the finite and essential parts of the persistence diagrams are provided separately: each element of this list is a tuple of size two that contains the finite and essential parts of the corresponding persistence diagram, of shapes [num_finite_points, 2] and [num_essential_points, 1] respectively
+            List[Tuple[tf.Tensor,tf.Tensor]]: List of lower-star persistence diagrams. The length of this list is the same than that of dimensions, i.e., there is one persistence diagram per homology dimension provided in the input list dimensions. Moreover, the finite and essential parts of the persistence diagrams are provided separately: each element of this list is a tuple of size two that contains the finite and essential parts of the corresponding persistence diagram, of shapes [num_finite_points, 2] and [num_essential_points, 1] respectively
         """
         # Don't try to compute gradients for the vertex pairs
         indices = _LowerStarSimplexTree(self.simplextree, filtration.numpy(), self.dimensions)
diff --git a/src/python/gudhi/tensorflow/rips_layer.py b/src/python/gudhi/tensorflow/rips_layer.py
index b5b58ab4..97c2692d 100644
--- a/src/python/gudhi/tensorflow/rips_layer.py
+++ b/src/python/gudhi/tensorflow/rips_layer.py
@@ -63,7 +63,7 @@ class RipsLayer(tf.keras.layers.Layer):
             X (TensorFlow variable): point cloud of shape [number of points, number of dimensions]
 
         Returns:
-            dgms (list of tuple of TensorFlow variables): list of Rips persistence diagrams. The length of this list is the same than that of dimensions, i.e., there is one persistence diagram per homology dimension provided in the input list dimensions. Moreover, the finite and essential parts of the persistence diagrams are provided separately: each element of this list is a tuple of size two that contains the finite and essential parts of the corresponding persistence diagram, of shapes [num_finite_points, 2] and [num_essential_points, 1] respectively
+            List[Tuple[tf.Tensor,tf.Tensor]]: List of Rips persistence diagrams. The length of this list is the same than that of dimensions, i.e., there is one persistence diagram per homology dimension provided in the input list dimensions. Moreover, the finite and essential parts of the persistence diagrams are provided separately: each element of this list is a tuple of size two that contains the finite and essential parts of the corresponding persistence diagram, of shapes [num_finite_points, 2] and [num_essential_points, 1] respectively
         """    
         # Compute distance matrix
         DX = tf.norm(tf.expand_dims(X, 1)-tf.expand_dims(X, 0), axis=2)