Format code with black

Author: jameschapman19

proxtorch/operators/graphnet.py

@@ -15,9 +15,9 @@ def prox(self, x: torch.Tensor, tau: float) -> torch.Tensor:
 
     def _smooth(self, x: torch.Tensor) -> torch.Tensor:
         # The last channel is the for the l1 norm
-        grad = self.gradient(x)[:-1]/(1-self.l1_ratio)
+        grad = self.gradient(x)[:-1] / (1 - self.l1_ratio)
         # sum of squares of the gradients
-        norm = torch.sum(grad ** 2)
+        norm = torch.sum(grad**2)
         return 0.5 * norm * self.alpha * (1 - self.l1_ratio)
 
     def _nonsmooth(self, x: torch.Tensor) -> torch.Tensor:

test/test_graphnet.py

@@ -5,6 +5,7 @@
 
 def test_converges_to_sparse_smooth():
     import matplotlib.pyplot as plt
+
     torch.manual_seed(0)
 
     # generate a spatially sparse signal
@@ -39,7 +40,7 @@ def objective(x):
     # optimization loop
     for i in range(20000):
         optimizer.zero_grad()
-        p=prox(x)
+        p = prox(x)
         obj = objective(x) + prox(x)
         obj.backward()
         optimizer.step()