Autofix linting and type checks

Signed-off-by: Kheil-Z <[email protected]>

Author: Kheil-Z

monai/networks/blocks/warp.py

@@ -152,20 +152,30 @@ def forward(
             )
         else:
             # using csrc resampling
-            warped_image = grid_pull(image, grid, bound=self._padding_mode, extrapolate=True, interpolation=self._interp_mode)
+            warped_image = grid_pull(
+                image, grid, bound=self._padding_mode, extrapolate=True, interpolation=self._interp_mode
+            )
         if keypoints is not None:
             with torch.no_grad():
                 offset = torch.as_tensor(image.shape[2:]).to(keypoints) / 2.0
                 offset = offset.unsqueeze(0).unsqueeze(0)
                 normalized_keypoints = torch.flip((keypoints - offset) / offset, (-1,))
-            ddf_keypoints = F.grid_sample(
-                ddf, normalized_keypoints.view(batch_size, -1, 1, 1, spatial_dims),
-                mode=self._interp_mode, padding_mode=f"{self._padding_mode}", align_corners=True
-            ).view(batch_size, 3, -1).permute((0, 2, 1))
+            ddf_keypoints = (
+                F.grid_sample(
+                    ddf,
+                    normalized_keypoints.view(batch_size, -1, 1, 1, spatial_dims),
+                    mode=self._interp_mode,
+                    padding_mode=f"{self._padding_mode}",
+                    align_corners=True,
+                )
+                .view(batch_size, 3, -1)
+                .permute((0, 2, 1))
+            )
             warped_keypoints = keypoints + ddf_keypoints
             return warped_image, warped_keypoints
         return warped_image
 
+
 class DVF2DDF(nn.Module):
     """
     Layer calculates a dense displacement field (DDF) from a dense velocity field (DVF)

monai/networks/nets/voxelmorph.py

@@ -445,7 +445,7 @@ def forward(
         moving: torch.Tensor,
         fixed: torch.Tensor,
         moving_seg: torch.Tensor | None = None,
-        fixed_keypoints: torch.Tensor | None = None
+        fixed_keypoints: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, ...]:
         if moving.shape != fixed.shape:
             raise ValueError(

tests/networks/nets/test_voxelmorph.py

@@ -277,8 +277,12 @@ def test_shape(self, input_param, input_shape, expected_shape):
     def test_shape_seg(
         self,
         input_param,
-        moving_shape, fixed_shape, moving_seg_shape,
-        expected_warped_moving_shape, expected_warped_moving_seg_shape, expected_ddf_shape
+        moving_shape,
+        fixed_shape,
+        moving_seg_shape,
+        expected_warped_moving_shape,
+        expected_warped_moving_seg_shape,
+        expected_ddf_shape,
     ):
         net = VoxelMorph(**input_param).to(device)
         with eval_mode(net):
@@ -325,5 +329,6 @@ def test_ill_input_seg_shape(self, input_param, moving_shape, fixed_shape, movin
                     torch.randn(moving_seg_shape).to(device),
                 )
 
+
 if __name__ == "__main__":
     unittest.main()