NoOp

>>> import numpy as np
>>> import albumentations as A
>>>
>>> # Prepare sample data
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> mask = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
>>> bboxes = np.array([[10, 10, 50, 50], [40, 40, 80, 80]], dtype=np.float32)
>>> bbox_labels = [1, 2]
>>> keypoints = np.array([[20, 30], [60, 70]], dtype=np.float32)
>>> keypoint_labels = [0, 1]
>>>
>>> # Create transform pipeline with NoOp
>>> transform = A.Compose([
...     A.NoOp(p=1.0),  # Always applied, but does nothing
... ], bbox_params=A.BboxParams(coord_format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(coord_format='xy', label_fields=['keypoint_labels']))
>>>
>>> # Apply the transform
>>> transformed = transform(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Verify nothing has changed
>>> np.array_equal(image, transformed['image'])  # True
>>> np.array_equal(mask, transformed['mask'])  # True
>>> np.array_equal(bboxes, transformed['bboxes'])  # True
>>> np.array_equal(keypoints, transformed['keypoints'])  # True
>>> bbox_labels == transformed['bbox_labels']  # True
>>> keypoint_labels == transformed['keypoint_labels']  # True
>>>
>>> # NoOp is often used as a placeholder or for testing
>>> # For example, in conditional transforms:
>>> condition = False  # Some condition
>>> transform = A.Compose([
...     A.HorizontalFlip(p=1.0) if condition else A.NoOp(p=1.0)
... ])