apply linting

Author: AntoineSimoulin

test/common_utils.py

@@ -469,7 +469,7 @@ def sample_position(values, max_value):
         raise ValueError(f"Format {format} is not supported")
     out_boxes = torch.stack(parts, dim=-1).to(dtype=dtype, device=device)
     if tv_tensors.is_rotated_bounding_format(format):
-        # Rotated bounding boxes are not inherently confined within the canvas, so clamping is applied. 
+        # Rotated bounding boxes are not inherently confined within the canvas, so clamping is applied.
         # Transform tests allow a 2-pixel tolerance relative to the canvas size.
         # To prevent discrepancies when clamping with different canvas sizes, we add a 2-pixel buffer.
         buffer = 4

test/test_transforms_v2.py

@@ -4413,19 +4413,14 @@ def _reference_resized_crop_bounding_boxes(self, bounding_boxes, *, top, left, h
                 [0, 0, 1],
             ],
         )
-        affine_matrix = (resize_affine_matrix @ crop_affine_matrix)[:2, :]
 
         helper = (
             reference_affine_rotated_bounding_boxes_helper
             if tv_tensors.is_rotated_bounding_format(bounding_boxes.format)
             else reference_affine_bounding_boxes_helper
         )
 
-        bounding_boxes = helper(
-            bounding_boxes,
-            affine_matrix=crop_affine_matrix,
-            new_canvas_size=(height, width)
-        )
+        bounding_boxes = helper(bounding_boxes, affine_matrix=crop_affine_matrix, new_canvas_size=(height, width))
 
         return helper(
             bounding_boxes,