fix doc model part 2

pina/model/block/utils_convolution.py

@@ -1,5 +1,5 @@
 """
-TODO
+Module for utility functions for the convolutional layer.
 """
 
 import torch
@@ -7,7 +7,13 @@ import torch
 
 def check_point(x, current_stride, dim):
     """
-    TODO
+    Check if the point is in the current stride.
+
+    :param torch.Tensor x: The input data.
+    :param int current_stride: The current stride.
+    :param int dim: The shape of the filter.
+    :return: The indeces of the points in the current stride.
+    :rtype: torch.Tensor
     """
     max_stride = current_stride + dim
     indeces = torch.logical_and(
@@ -17,13 +23,12 @@ def check_point(x, current_stride, dim):
 
 
 def map_points_(x, filter_position):
-    """Mapping function n dimensional case
+    """
+    The mapping function for n-dimensional case.
 
-    :param x: input data of two dimension
-    :type x: torch.tensor
-    :param filter_position: position of the filter
-    :type dim: list[numeric]
-    :return: data mapped inplace
+    :param torch.Tensor x: The two-dimensional input data.
+    :param list[int] filter_position: The position of the filter.
+    :return: The data mapped in-place.
     :rtype: torch.tensor
     """
     x.add_(-filter_position)
@@ -32,14 +37,20 @@ def map_points_(x, filter_position):
 
 
 def optimizing(f):
-    """Decorator for calling a function just once
+    """
+    Decorator to call the function only once.
 
     :param f: python function
-    :type f: function
+    :type f: Callable
     """
 
     def wrapper(*args, **kwargs):
+        """
+        Wrapper function.
 
+        :param args: The arguments of the function.
+        :param kwargs: The keyword arguments of the function.
+        """
         if kwargs["type_"] == "forward":
             if not wrapper.has_run_inverse:
                 wrapper.has_run_inverse = True