docstring improvement

mrmustard/lab_dev/transformations/grn.py

@@ -31,6 +31,26 @@
 class GRN(Channel):
     r"""
     The Gaussian random noise channel.
+
+    Y_train determines whether or not the Y matrix is trainable.
+    The number of modes must match half of the size of the Y matrix.
+    .. code-block ::
+
+        >>> import numpy as np
+        >>> from mrmustard.lab_dev import GRN
+
+        >>> channel = GRN(modes=[1, 2], Y = .2 * np.eye(4))
+        >>> assert channel.modes == [1, 2]
+        >>> assert np.allclose(channel.Y.value, .2 * np.eye(4))
+    
+    Args:
+        modes: The modes the channel is applied to
+        Y: The Y matrix of the Gaussian random noise
+        Y_train: whether the Y matrix is a trainable variable
+    
+    ..details::
+        The Bargmann representation of the channel is computed via the formulas provided in the paper:
+        https://arxiv.org/pdf/2209.06069
     """
 
     short_name = "GRN"

mrmustard/physics/triples.py

@@ -607,7 +607,13 @@ def fock_damping_Abc(
 
 def gaussian_random_noise_Abc(Y: RealMatrix) -> Union[Matrix, Vector, Scalar]:
     r"""
-    The (A, b, c) for the gaussian random noise channel.
+    The triple (A, b, c) for the gaussian random noise channel.
+
+    Args:
+        Y: the Y matrix of the Gaussian random noise channel.
+
+    Returns:
+        The ``(A, b, c)`` triple of the Gaussian random noise channel.
     """
     m = Y.shape[-1] // 2
     xi = math.eye(2 * m) + Y / settings.HBAR