Fix test formatting with autofix

Author: kephale

tests/test_dice_focal_loss.py

@@ -91,28 +91,25 @@ def test_script(self):
         test_input = torch.ones(2, 1, 8, 8)
         test_script_save(loss, test_input, test_input)
 
-    @parameterized.expand([
-        ("sum_None_0.5_0.25", "sum", None, 0.5, 0.25),
-        ("sum_weight_0.5_0.25", "sum", torch.tensor([1.0, 1.0, 2.0]), 0.5, 0.25),
-        ("sum_weight_tuple_0.5_0.25", "sum", (3, 2.0, 1), 0.5, 0.25),
-        ("mean_None_0.5_0.25", "mean", None, 0.5, 0.25),
-        ("mean_weight_0.5_0.25", "mean", torch.tensor([1.0, 1.0, 2.0]), 0.5, 0.25),
-        ("mean_weight_tuple_0.5_0.25", "mean", (3, 2.0, 1), 0.5, 0.25),
-        ("none_None_0.5_0.25", "none", None, 0.5, 0.25),
-        ("none_weight_0.5_0.25", "none", torch.tensor([1.0, 1.0, 2.0]), 0.5, 0.25),
-        ("none_weight_tuple_0.5_0.25", "none", (3, 2.0, 1), 0.5, 0.25),
-    ])
+    @parameterized.expand(
+        [
+            ("sum_None_0.5_0.25", "sum", None, 0.5, 0.25),
+            ("sum_weight_0.5_0.25", "sum", torch.tensor([1.0, 1.0, 2.0]), 0.5, 0.25),
+            ("sum_weight_tuple_0.5_0.25", "sum", (3, 2.0, 1), 0.5, 0.25),
+            ("mean_None_0.5_0.25", "mean", None, 0.5, 0.25),
+            ("mean_weight_0.5_0.25", "mean", torch.tensor([1.0, 1.0, 2.0]), 0.5, 0.25),
+            ("mean_weight_tuple_0.5_0.25", "mean", (3, 2.0, 1), 0.5, 0.25),
+            ("none_None_0.5_0.25", "none", None, 0.5, 0.25),
+            ("none_weight_0.5_0.25", "none", torch.tensor([1.0, 1.0, 2.0]), 0.5, 0.25),
+            ("none_weight_tuple_0.5_0.25", "none", (3, 2.0, 1), 0.5, 0.25),
+        ]
+    )
     def test_with_alpha(self, name, reduction, weight, lambda_focal, alpha):
         size = [3, 3, 5, 5]
         label = torch.randint(low=0, high=2, size=size)
         pred = torch.randn(size)
 
-        common_params = {
-            "include_background": True,
-            "to_onehot_y": False,
-            "reduction": reduction,
-            "weight": weight,
-        }
+        common_params = {"include_background": True, "to_onehot_y": False, "reduction": reduction, "weight": weight}
 
         dice_focal = DiceFocalLoss(gamma=1.0, lambda_focal=lambda_focal, alpha=alpha, **common_params)
         dice = DiceLoss(**common_params)
@@ -123,5 +120,6 @@ def test_with_alpha(self, name, reduction, weight, lambda_focal, alpha):
 
         np.testing.assert_allclose(result, expected_val, err_msg=f"Failed on case: {name}")
 
+
 if __name__ == "__main__":
     unittest.main()