🎨 Format Python code with psf/black

pina/adaptive_functions/adaptive_sin.py

@@ -3,7 +3,7 @@ from torch.nn.parameter import Parameter
 
 
 class AdaptiveSin(torch.nn.Module):
-    '''
+    """
     Implementation of soft exponential activation.
     Shape:
         - Input: (N, *) where * means, any number of additional
@@ -18,35 +18,37 @@ class AdaptiveSin(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(AdaptiveSin, self).__init__()
 
         # initialize alpha
         self.alpha = Parameter(
-            torch.normal(torch.tensor(1.0),
-                         torch.tensor(0.1)))  # create a tensor out of alpha
+            torch.normal(torch.tensor(1.0), torch.tensor(0.1))
+        )  # create a tensor out of alpha
         self.alpha.requiresGrad = True  # set requiresGrad to true!
 
         self.scale = Parameter(
-            torch.normal(torch.tensor(1.0), torch.tensor(0.1)))
+            torch.normal(torch.tensor(1.0), torch.tensor(0.1))
+        )
         self.scale.requiresGrad = True  # set requiresGrad to true!
 
         self.translate = Parameter(
-            torch.normal(torch.tensor(0.0), torch.tensor(0.1)))
+            torch.normal(torch.tensor(0.0), torch.tensor(0.1))
+        )
         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 * (torch.sin(self.alpha * x + self.translate))