fix doc loss and codacy

pina/loss/scalar_weighting.py

@@ -1,20 +1,41 @@
-"""Module for Loss Interface"""
+"""Module for the Scalar Weighting."""
 
 from .weighting_interface import WeightingInterface
 from ..utils import check_consistency
 
 
 class _NoWeighting(WeightingInterface):
+    """
+    Weighting scheme that does not apply any weighting to the losses.
+    """
+
     def aggregate(self, losses):
+        """
+        Aggregate the losses.
+
+        :param dict losses: The dictionary of losses.
+        :return: The aggregated losses.
+        :rtype: torch.Tensor
+        """
         return sum(losses.values())
 
 
 class ScalarWeighting(WeightingInterface):
     """
-    TODO
+    Weighting scheme that assigns a scalar weight to each loss term.
     """
 
     def __init__(self, weights):
+        """
+        Initialization of the :class:`ScalarWeighting` class.
+
+        :param weights: The weights to be assigned to each loss term.
+            If a single scalar value is provided, it is assigned to all loss
+            terms. If a dictionary is provided, the keys are the conditions and
+            the values are the weights. If a condition is not present in the
+            dictionary, the default value is used.
+        :type weights: float | int | dict
+        """
         super().__init__()
         check_consistency([weights], (float, dict, int))
         if isinstance(weights, (float, int)):
@@ -28,8 +49,8 @@ class ScalarWeighting(WeightingInterface):
         """
         Aggregate the losses.
 
-        :param dict(torch.Tensor) losses: The dictionary of losses.
-        :return: The losses aggregation. It should be a scalar Tensor.
+        :param dict losses: The dictionary of losses.
+        :return: The aggregated losses.
         :rtype: torch.Tensor
         """
         return sum(