Applied ufmt

Author: Alex-S-H-P

torchvision/transforms/v2/functional/_geometry.py

@@ -2677,9 +2677,7 @@ def five_crop_video(
     return five_crop_image(video, size)
 
 
-def ten_crop(
-    inpt: torch.Tensor, size: list[int], vertical_flip: bool = False
-) -> tuple[
+def ten_crop(inpt: torch.Tensor, size: list[int], vertical_flip: bool = False) -> tuple[
     torch.Tensor,
     torch.Tensor,
     torch.Tensor,
@@ -2703,9 +2701,7 @@ def ten_crop(
 
 @_register_five_ten_crop_kernel_internal(ten_crop, torch.Tensor)
 @_register_five_ten_crop_kernel_internal(ten_crop, tv_tensors.Image)
-def ten_crop_image(
-    image: torch.Tensor, size: list[int], vertical_flip: bool = False
-) -> tuple[
+def ten_crop_image(image: torch.Tensor, size: list[int], vertical_flip: bool = False) -> tuple[
     torch.Tensor,
     torch.Tensor,
     torch.Tensor,
@@ -2730,9 +2726,7 @@ def ten_crop_image(
 
 
 @_register_five_ten_crop_kernel_internal(ten_crop, PIL.Image.Image)
-def _ten_crop_image_pil(
-    image: PIL.Image.Image, size: list[int], vertical_flip: bool = False
-) -> tuple[
+def _ten_crop_image_pil(image: PIL.Image.Image, size: list[int], vertical_flip: bool = False) -> tuple[
     PIL.Image.Image,
     PIL.Image.Image,
     PIL.Image.Image,
@@ -2757,9 +2751,7 @@ def _ten_crop_image_pil(
 
 
 @_register_five_ten_crop_kernel_internal(ten_crop, tv_tensors.Video)
-def ten_crop_video(
-    video: torch.Tensor, size: list[int], vertical_flip: bool = False
-) -> tuple[
+def ten_crop_video(video: torch.Tensor, size: list[int], vertical_flip: bool = False) -> tuple[
     torch.Tensor,
     torch.Tensor,
     torch.Tensor,

torchvision/transforms/v2/functional/_meta.py

@@ -196,7 +196,7 @@ def convert_bounding_boxes_to_points(bounding_boxes: tv_tensors.BoundingBoxes) -
 
         This handles rotated :class:`tv_tensors.BoundingBoxes` formats
         by first converting them to XYXYXYXY format.
-         
+
         Due to floating-point approximation, this may not be an exact computation.
 
     Args:
@@ -213,9 +213,7 @@ def convert_bounding_boxes_to_points(bounding_boxes: tv_tensors.BoundingBoxes) -
             new_format=BoundingBoxFormat.XYXYXYXY,
             inplace=False,
         )
-        return tv_tensors.KeyPoints(
-            _xyxyxyxy_to_keypoints(bbox), canvas_size=bounding_boxes.canvas_size
-        )
+        return tv_tensors.KeyPoints(_xyxyxyxy_to_keypoints(bbox), canvas_size=bounding_boxes.canvas_size)
 
     bbox = _convert_bounding_box_format(
         bounding_boxes.as_subclass(torch.Tensor),

torchvision/tv_tensors/_keypoints.py

@@ -72,8 +72,7 @@ def __init__(
             dtype: Optional[torch.dtype] = None,
             device: Optional[Union[torch.device, str, int]] = None,
             requires_grad: Optional[bool] = None,
-        ):
-            ...
+        ): ...
 
     @classmethod
     def _wrap_output(