[MONAI] code formatting

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>

monai/apps/detection/transforms/array.py

@@ -266,7 +266,8 @@ def __init__(self, spatial_size: Sequence[int] | int, size_mode: str = "all", **
         self.size_mode = look_up_option(size_mode, ["all", "longest"])
         self.spatial_size = spatial_size
 
-    def __call__(self, boxes: NdarrayOrTensor, src_spatial_size: Sequence[int] | int) -> NdarrayOrTensor:  # type: ignore[override]
+    # type: ignore[override]
+    def __call__(self, boxes: NdarrayOrTensor, src_spatial_size: Sequence[int] | int) -> NdarrayOrTensor:
         """
         Args:
             boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
@@ -533,7 +534,8 @@ class RotateBox90(Rotate90):
 
     backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
-    def __call__(self, boxes: NdarrayTensor, spatial_size: Sequence[int] | int) -> NdarrayTensor:  # type: ignore[override]
+    # type: ignore[override]
+    def __call__(self, boxes: NdarrayTensor, spatial_size: Sequence[int] | int) -> NdarrayTensor:
         """
         Args:
             img: channel first array, must have shape: (num_channels, H[, W, ..., ]),

monai/apps/detection/transforms/box_ops.py

@@ -185,7 +185,9 @@ def flip_boxes(
     flip_axes = ensure_tuple(flip_axes)
 
     # flip box
-    _flip_boxes: NdarrayTensor = boxes.clone() if isinstance(boxes, torch.Tensor) else deepcopy(boxes)  # type: ignore[assignment]
+    _flip_boxes: NdarrayTensor = (
+        boxes.clone() if isinstance(boxes, torch.Tensor) else deepcopy(boxes)
+    )  # type: ignore[assignment]
 
     for axis in flip_axes:
         _flip_boxes[:, axis + spatial_dims] = spatial_size[axis] - boxes[:, axis] - TO_REMOVE

monai/metrics/rocauc.py

@@ -54,7 +54,8 @@ def __init__(self, average: Average | str = Average.MACRO) -> None:
         super().__init__()
         self.average = average
 
-    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:  # type: ignore[override]
+    # type: ignore[override]
+    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         return y_pred, y
 
     def aggregate(self, average: Average | str | None = None) -> np.ndarray | float | npt.ArrayLike:

monai/metrics/utils.py

@@ -162,8 +162,12 @@ def get_mask_edges(
         seg_pred, seg_gt = np.expand_dims(seg_pred, axis=channel_dim), np.expand_dims(seg_gt, axis=channel_dim)
         box_start, box_end = generate_spatial_bounding_box(np.asarray(seg_pred | seg_gt))
         cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
-        seg_pred = convert_data_type(np.squeeze(cropper(seg_pred), axis=channel_dim), np.ndarray)[0]  # type: ignore[arg-type]
-        seg_gt = convert_data_type(np.squeeze(cropper(seg_gt), axis=channel_dim), np.ndarray)[0]  # type: ignore[arg-type]
+        seg_pred = convert_data_type(np.squeeze(cropper(seg_pred), axis=channel_dim), np.ndarray)[
+            0
+        ]  # type: ignore[arg-type]
+        seg_gt = convert_data_type(np.squeeze(cropper(seg_gt), axis=channel_dim), np.ndarray)[
+            0
+        ]  # type: ignore[arg-type]
 
     # Do binary erosion and use XOR to get edges
     edges_pred = binary_erosion(seg_pred) ^ seg_pred