🎨 Format Python code with psf/black

pina/adaptive_functions/adaptive_square.py

@@ -3,7 +3,7 @@ from torch.nn.parameter import Parameter
 
 
 class AdaptiveSquare(torch.nn.Module):
-    '''
+    """
     Implementation of soft exponential activation.
     Shape:
         - Input: (N, *) where * means, any number of additional
@@ -18,16 +18,16 @@ class AdaptiveSquare(torch.nn.Module):
         >>> a1 = soft_exponential(256)
         >>> x = torch.randn(256)
         >>> x = a1(x)
-    '''
+    """
 
     def __init__(self, alpha=None):
-        '''
+        """
         Initialization.
         INPUT:
             - in_features: shape of the input
             - aplha: trainable parameter
             aplha is initialized with zero value by default
-        '''
+        """
         super(AdaptiveSquare, self).__init__()
 
         self.scale = Parameter(torch.tensor(1.0))
@@ -37,8 +37,8 @@ class AdaptiveSquare(torch.nn.Module):
         self.translate.requiresGrad = True  # set requiresGrad to true!
 
     def forward(self, x):
-        '''
+        """
         Forward pass of the function.
         Applies the function to the input elementwise.
-        '''
-        return self.scale * (x + self.translate)**2
+        """
+        return self.scale * (x + self.translate) ** 2