Fix Codacy Warnings (#477)

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>

pina/label_tensor.py

@@ -17,9 +17,15 @@ class LabelTensor(torch.Tensor):
 
     @property
     def tensor(self):
+        """
+        Give the tensor part of the LabelTensor.
+
+        :return: tensor part of the LabelTensor
+        :rtype: torch.Tensor
+        """
         return self.as_subclass(Tensor)
 
-    def __init__(self, x, labels, **kwargs):
+    def __init__(self, x, labels):
         """
         Construct a `LabelTensor` by passing a dict of the labels
 
@@ -43,8 +49,9 @@ class LabelTensor(torch.Tensor):
         :return: labels of self
         :rtype: list
         """
-        if self.ndim - 1 in self._labels.keys():
+        if self.ndim - 1 in self._labels:
             return self._labels[self.ndim - 1]["dof"]
+        return None
 
     @property
     def full_labels(self):
@@ -55,11 +62,11 @@ class LabelTensor(torch.Tensor):
         """
         to_return_dict = {}
         shape_tensor = self.shape
-        for i in range(len(shape_tensor)):
-            if i in self._labels.keys():
+        for i, value in enumerate(shape_tensor):
+            if i in self._labels:
                 to_return_dict[i] = self._labels[i]
             else:
-                to_return_dict[i] = {"dof": range(shape_tensor[i]), "name": i}
+                to_return_dict[i] = {"dof": range(value), "name": i}
         return to_return_dict
 
     @property
@@ -186,7 +193,7 @@ class LabelTensor(torch.Tensor):
         labels = copy(self._labels)
 
         # Get the dimension names and the respective dimension index
-        dim_names = {labels[dim]["name"]: dim for dim in labels.keys()}
+        dim_names = {labels[dim]["name"]: dim for dim in labels}
         ndim = super().ndim
         tensor = self.tensor.as_subclass(torch.Tensor)
 
@@ -259,7 +266,7 @@ class LabelTensor(torch.Tensor):
 
         # Check label consistency across tensors, excluding the
         # concatenation dimension
-        for key in tensors_labels[0].keys():
+        for key in tensors_labels[0]:
             if key != dim:
                 if any(
                     tensors_labels[i][key] != tensors_labels[0][key]
@@ -325,6 +332,12 @@ class LabelTensor(torch.Tensor):
 
     @property
     def dtype(self):
+        """
+        Give the dtype of the tensor.
+
+        :return: dtype of the tensor
+        :rtype: torch.dtype
+        """
         return super().dtype
 
     def to(self, *args, **kwargs):
@@ -350,12 +363,31 @@ class LabelTensor(torch.Tensor):
         return out
 
     def append(self, tensor, mode="std"):
+        """
+        Appends a given tensor to the current tensor along the last dimension.
+
+        This method allows for two types of appending operations:
+        1. **Standard append** ("std"): Concatenates the tensors along the
+            last dimension.
+        2. **Cross append** ("cross"): Repeats the current tensor and the new
+            tensor in a cross-product manner, then concatenates them.
+
+        :param LabelTensor tensor: The tensor to append.
+        :param mode: The append mode to use. Defaults to "std".
+        :type mode: str, optional
+        :return: The new tensor obtained by appending the input tensor
+            (either 'std' or 'cross').
+        :rtype: LabelTensor
+
+        :raises ValueError: If the mode is not "std" or "cross".
+        """
         if mode == "std":
             # Call cat on last dimension
             new_label_tensor = LabelTensor.cat(
                 [self, tensor], dim=self.ndim - 1
             )
-        elif mode == "cross":
+            return new_label_tensor
+        if mode == "cross":
             # Crete tensor and call cat on last dimension
             tensor1 = self
             tensor2 = tensor
@@ -368,9 +400,8 @@ class LabelTensor(torch.Tensor):
             new_label_tensor = LabelTensor.cat(
                 [tensor1, tensor2], dim=self.ndim - 1
             )
-        else:
-            raise ValueError('mode must be either "std" or "cross"')
-        return new_label_tensor
+            return new_label_tensor
+        raise ValueError('mode must be either "std" or "cross"')
 
     @staticmethod
     def vstack(label_tensors):
@@ -461,7 +492,7 @@ class LabelTensor(torch.Tensor):
         # Update labels based on the index
         offset = 0
         for dim, idx in enumerate(index):
-            if dim in self.stored_labels.keys():
+            if dim in self.stored_labels:
                 if isinstance(idx, int):
                     selected_tensor = selected_tensor.unsqueeze(dim)
                 if idx != slice(None):
@@ -508,7 +539,7 @@ class LabelTensor(torch.Tensor):
         indexer = [slice(None)] * self.ndim
         # Assigned the sorted index to the specified dimension
         indexer[dim] = sorted_index
-        return self.__getitem__(tuple(indexer))
+        return self[tuple(indexer)]
 
     def __deepcopy__(self, memo):
         """
@@ -539,7 +570,7 @@ class LabelTensor(torch.Tensor):
         # Update lables
         labels = self._labels
         keys_list = list(*dims)
-        labels = {keys_list.index(k): labels[k] for k in labels.keys()}
+        labels = {keys_list.index(k): v for k, v in labels.items()}
 
         # Assign labels to the new tensor
         tensor._labels = labels