>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # 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]
>>>
>>> # Example 1: Pad all sides by the same value
>>> transform = A.Compose([
...     A.Pad(padding=20, border_mode=cv2.BORDER_CONSTANT, fill=0),
... ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(format='xy', label_fields=['keypoint_labels']))
>>>
>>> # Apply the transform
>>> padded = transform(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Get the padded data
>>> padded_image = padded['image']  # Shape will be (140, 140, 3)
>>> padded_mask = padded['mask']    # Shape will be (140, 140)
>>> padded_bboxes = padded['bboxes']  # Bounding boxes coordinates adjusted to the padded image
>>> padded_keypoints = padded['keypoints']  # Keypoints coordinates adjusted to the padded image
>>>
>>> # Example 2: Different padding for sides using (pad_x, pad_y)
>>> transform_xy = A.Compose([
...     A.Pad(
...         padding=(10, 30),  # 10px padding on left/right, 30px on top/bottom
...         border_mode=cv2.BORDER_CONSTANT,
...         fill=128  # Gray padding color
...     ),
... ])
>>>
>>> padded_xy = transform_xy(image=image)
>>> padded_xy_image = padded_xy['image']  # Shape will be (160, 120, 3)
>>>
>>> # Example 3: Different padding for each side
>>> transform_sides = A.Compose([
...     A.Pad(
...         padding=(5, 10, 15, 20),  # (left, top, right, bottom)
...         border_mode=cv2.BORDER_CONSTANT,
...         fill=0,
...         fill_mask=0
...     ),
... ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['bbox_labels']))
>>>
>>> padded_sides = transform_sides(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels
... )
>>>
>>> padded_sides_image = padded_sides['image']  # Shape will be (130, 120, 3)
>>> padded_sides_bboxes = padded_sides['bboxes']  # Bounding boxes adjusted to the new coordinates
>>>
>>> # Example 4: Using different border_mode options
>>> # Create a smaller image for better visualization of reflection/wrapping
>>> small_image = np.random.randint(0, 256, (10, 10, 3), dtype=np.uint8)
>>>
>>> # Reflection padding
>>> reflect_pad = A.Compose([
...     A.Pad(padding=5, border_mode=cv2.BORDER_REFLECT_101),
... ])
>>> reflected = reflect_pad(image=small_image)
>>> reflected_image = reflected['image']  # Shape will be (20, 20, 3) with reflected edges
>>>
>>> # Replicate padding
>>> replicate_pad = A.Compose([
...     A.Pad(padding=5, border_mode=cv2.BORDER_REPLICATE),
... ])
>>> replicated = replicate_pad(image=small_image)
>>> replicated_image = replicated['image']  # Shape will be (20, 20, 3) with replicated edges
>>>
>>> # Example 5: Padding with masks and constant border mode
>>> binary_mask = np.zeros((50, 50), dtype=np.uint8)
>>> binary_mask[10:40, 10:40] = 1  # Set center region to 1
>>>
>>> mask_transform = A.Compose([
...     A.Pad(
...         padding=10,
...         border_mode=cv2.BORDER_CONSTANT,
...         fill=0,          # Black padding for image
...         fill_mask=0      # Use 0 for mask padding (background)
...     ),
... ])
>>>
>>> padded_mask_result = mask_transform(image=image, mask=binary_mask)
>>> padded_binary_mask = padded_mask_result['mask']  # Shape will be (70, 70)
Parameters
