🎨 Format Python code with psf/black

pina/adaptive_functions/adaptive_tanh.py

@@ -3,7 +3,7 @@ from torch.nn.parameter import Parameter
 
 
 class AdaptiveTanh(torch.nn.Module):
-    '''
+    """
     Implementation of soft exponential activation.
     Shape:
         - Input: (N, *) where * means, any number of additional
@@ -18,26 +18,28 @@ class AdaptiveTanh(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(AdaptiveTanh, self).__init__()
-        #self.in_features = in_features
+        # self.in_features = in_features
 
         # initialize alpha
         if alpha == None:
             self.alpha = Parameter(
-                torch.tensor(1.0))  # create a tensor out of alpha
+                torch.tensor(1.0)
+            )  # create a tensor out of alpha
         else:
             self.alpha = Parameter(
-                torch.tensor(alpha))  # create a tensor out of alpha
+                torch.tensor(alpha)
+            )  # create a tensor out of alpha
 
         self.alpha.requiresGrad = True  # set requiresGrad to true!
 
@@ -48,11 +50,13 @@ class AdaptiveTanh(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.
-        '''
+        """
         x += self.translate
-        return self.scale * (torch.exp(self.alpha * x) - torch.exp(
-            -self.alpha * x)) / (torch.exp(self.alpha * x) +
-                                 torch.exp(-self.alpha * x))
+        return (
+            self.scale
+            * (torch.exp(self.alpha * x) - torch.exp(-self.alpha * x))
+            / (torch.exp(self.alpha * x) + torch.exp(-self.alpha * x))
+        )