🎨 Format Python code with psf/black

pina/adaptive_functions/adaptive_relu.py

@@ -3,7 +3,7 @@ from torch.nn.parameter import Parameter
 
 
 class AdaptiveReLU(torch.nn.Module, Parameter):
-    '''
+    """
     Implementation of soft exponential activation.
     Shape:
         - Input: (N, *) where * means, any number of additional
@@ -18,16 +18,16 @@ class AdaptiveReLU(torch.nn.Module, Parameter):
         >>> a1 = soft_exponential(256)
         >>> x = torch.randn(256)
         >>> x = a1(x)
-    '''
+    """
 
     def __init__(self):
-        '''
+        """
         Initialization.
         INPUT:
             - in_features: shape of the input
             - aplha: trainable parameter
             aplha is initialized with zero value by default
-        '''
+        """
         super(AdaptiveReLU, self).__init__()
 
         self.scale = Parameter(torch.rand(1))
@@ -37,9 +37,9 @@ class AdaptiveReLU(torch.nn.Module, Parameter):
         self.translate.requiresGrad = True  # set requiresGrad to true!
 
     def forward(self, x):
-        '''
+        """
         Forward pass of the function.
         Applies the function to the input elementwise.
-        '''
-        #x += self.translate
+        """
+        # x += self.translate
         return torch.relu(x + self.translate) * self.scale