Apply black formatting to pass CI code style checks

Author: emanuilo

monai/transforms/croppad/dictionary.py

@@ -549,9 +549,12 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor], lazy: bool | None = No
 
         lazy_ = self.lazy if lazy is None else lazy
         self.cropper = SpatialCrop(
-            roi_center=roi_center, roi_size=roi_size,
-            roi_start=roi_start, roi_end=roi_end,
-            roi_slices=self._roi_slices, lazy=lazy_,
+            roi_center=roi_center,
+            roi_size=roi_size,
+            roi_start=roi_start,
+            roi_end=roi_end,
+            roi_slices=self._roi_slices,
+            lazy=lazy_,
         )
         for key in self.key_iterator(d):
             d[key] = self.cropper(d[key], lazy=lazy_)

tests/transforms/test_spatial_cropd.py

@@ -191,9 +191,7 @@ def test_inverse_with_string_keys(self):
     def test_pipeline_world_to_image_crop(self):
         """Integration test: TransformPointsWorldToImaged -> SpatialCropd with string keys."""
         # Create image with a 2x scaling affine: world coords = 2 * voxel coords
-        affine = torch.tensor(
-            [[2.0, 0, 0, 0], [0, 2.0, 0, 0], [0, 0, 2.0, 0], [0, 0, 0, 1.0]], dtype=torch.float64
-        )
+        affine = torch.tensor([[2.0, 0, 0, 0], [0, 2.0, 0, 0], [0, 0, 2.0, 0], [0, 0, 0, 1.0]], dtype=torch.float64)
         img = MetaTensor(torch.rand(1, 32, 32, 32), affine=affine)
 
         # World-space ROI: [4, 6, 8] to [20, 24, 28] -> voxel-space: [2, 3, 4] to [10, 12, 14]

tests/transforms/utility/test_transform_points_image_to_worldd.py

@@ -53,22 +53,16 @@ def test_matches_base_class(self, image, points, expected_output):
         """Verify that TransformPointsImageToWorldd produces the same result as
         ApplyTransformToPointsd with invert_affine=False."""
         data = {"image": image, "point": points}
-        convenience = TransformPointsImageToWorldd(
-            keys="point", refer_keys="image", dtype=torch.int64
-        )
-        base = ApplyTransformToPointsd(
-            keys="point", refer_keys="image", dtype=torch.int64, invert_affine=False
-        )
+        convenience = TransformPointsImageToWorldd(keys="point", refer_keys="image", dtype=torch.int64)
+        base = ApplyTransformToPointsd(keys="point", refer_keys="image", dtype=torch.int64, invert_affine=False)
         out_convenience = convenience(dict(data))
         out_base = base(dict(data))
         self.assertTrue(torch.allclose(out_convenience["point"], out_base["point"]))
 
     @parameterized.expand(TEST_CASES)
     def test_inverse(self, image, points, _expected_output):
         data = {"image": image, "point": points}
-        transform = TransformPointsImageToWorldd(
-            keys="point", refer_keys="image", dtype=torch.int64
-        )
+        transform = TransformPointsImageToWorldd(keys="point", refer_keys="image", dtype=torch.int64)
         output = transform(data)
         inverted = transform.inverse(output)
         self.assertTrue(torch.allclose(inverted["point"], points))