🎨 Format Python code with psf/black

pina/model/layers/residual.py

@@ -16,18 +16,20 @@ class ResidualBlock(nn.Module):
 
     """
 
-    def __init__(self,
-                 input_dim,
-                 output_dim,
-                 hidden_dim,
-                 spectral_norm=False,
-                 activation=torch.nn.ReLU()):
+    def __init__(
+        self,
+        input_dim,
+        output_dim,
+        hidden_dim,
+        spectral_norm=False,
+        activation=torch.nn.ReLU(),
+    ):
         """
         Initializes the ResidualBlock module.
 
         :param int input_dim: Dimension of the input to pass to the
             feedforward linear layer.
-        :param int output_dim: Dimension of the output from the 
+        :param int output_dim: Dimension of the output from the
             residual layer.
         :param int hidden_dim: Hidden dimension for mapping the input
             (first block).
@@ -82,6 +84,7 @@ class ResidualBlock(nn.Module):
 import torch
 import torch.nn as nn
 
+
 class EnhancedLinear(torch.nn.Module):
     """
     A wrapper class for enhancing a linear layer with activation and/or dropout.
@@ -132,8 +135,9 @@ class EnhancedLinear(torch.nn.Module):
             self._model = torch.nn.Sequential(layer, self._drop(dropout))
 
         elif (dropout is not None) and (activation is not None):
-            self._model = torch.nn.Sequential(layer, activation,
-                                              self._drop(dropout))
+            self._model = torch.nn.Sequential(
+                layer, activation, self._drop(dropout)
+            )
 
     def forward(self, x):
         """