DOC: Fix docstrings in gradient.py

pytensor/gradient.py

@@ -196,12 +196,13 @@ def Rop(
 
     Returns
     -------
+    :class:`~pytensor.graph.basic.Variable` or list/tuple of Variables
         A symbolic expression such obeying
         ``R_op[i] = sum_j (d f[i] / d wrt[j]) eval_point[j]``,
         where the indices in that expression are magic multidimensional
         indices that specify both the position within a list and all
         coordinates of the tensor elements.
-        If `wrt` is a list/tuple, then return a list/tuple with the results.
+        If `f` is a list/tuple, then return a list/tuple with the results.
     """
 
     if not isinstance(wrt, (list, tuple)):
@@ -384,6 +385,7 @@ def Lop(
 
     Returns
     -------
+    :class:`~pytensor.graph.basic.Variable` or list/tuple of Variables
         A symbolic expression satisfying
         ``L_op[i] = sum_i (d f[i] / d wrt[j]) eval_point[i]``
         where the indices in that expression are magic multidimensional
@@ -481,10 +483,10 @@ def grad(
 
     Returns
     -------
+    :class:`~pytensor.graph.basic.Variable` or list/tuple of Variables
         A symbolic expression for the gradient of `cost` with respect to each
         of the `wrt` terms.  If an element of `wrt` is not differentiable with
         respect to the output, then a zero variable is returned.
-
     """
     t0 = time.perf_counter()
 
@@ -701,7 +703,6 @@ def subgraph_grad(wrt, end, start=None, cost=None, details=False):
 
     Parameters
     ----------
-
     wrt : list of variables
         Gradients are computed with respect to `wrt`.
 
@@ -876,7 +877,6 @@ def _populate_var_to_app_to_idx(outputs, wrt, consider_constant):
 
     (A variable in consider_constant is not a function of
     anything)
-
     """
 
     # Validate and format consider_constant
@@ -1035,7 +1035,6 @@ def _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name=None):
     -------
     list of Variables
         A list of gradients corresponding to `wrt`
-
     """
     # build a dict mapping node to the terms node contributes to each of
     # its inputs' gradients
@@ -1423,8 +1422,9 @@ def access_grad_cache(var):
 
 
 def _float_zeros_like(x):
-    """Like zeros_like, but forces the object to have a
-    a floating point dtype"""
+    """Like zeros_like, but forces the object to have
+    a floating point dtype
+    """
 
     rval = x.zeros_like()
 
@@ -1436,7 +1436,8 @@ def _float_zeros_like(x):
 
 def _float_ones_like(x):
     """Like ones_like, but forces the object to have a
-    floating point dtype"""
+    floating point dtype
+    """
 
     dtype = x.type.dtype
     if dtype not in pytensor.tensor.type.float_dtypes:
@@ -1613,7 +1614,6 @@ def abs_rel_errors(self, g_pt):
 
         Corresponding ndarrays in `g_pt` and `self.gf` must have the same
         shape or ValueError is raised.
-
         """
         if len(g_pt) != len(self.gf):
             raise ValueError("argument has wrong number of elements", len(g_pt))
@@ -1740,7 +1740,6 @@ def verify_grad(
     This function does not support multiple outputs. In `tests.scan.test_basic`
     there is an experimental `verify_grad` that covers that case as well by
     using random projections.
-
     """
     from pytensor.compile.function import function
     from pytensor.compile.sharedvalue import shared
@@ -2267,7 +2266,6 @@ def grad_clip(x, lower_bound, upper_bound):
     -----
     We register an opt in tensor/opt.py that remove the GradClip.
     So it have 0 cost in the forward and only do work in the grad.
-
     """
     return GradClip(lower_bound, upper_bound)(x)