code: def horizontal_flip_with_boxes(
      prob: float, images: torch.Tensor, boxes: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      Perform horizontal flip on the given images and corresponding boxes.
      Args:
          prob (float): probility to flip the images.
          images (tensor): images to perform horizontal flip, the dimension is
              `channel` x `num frames` x `height` x `width`.
          boxes (tensor): Corresponding boxes to images.
              Dimension is `num boxes` x 4.
      Returns:
          images (tensor): images with dimension of
              `channel` x `num frames` x `height` x `width`.
          flipped_boxes (tensor): the flipped boxes with dimension of
              `num boxes` x 4.
      """
      flipped_boxes = copy.deepcopy(boxes)
  
      if np.random.uniform() < prob:
          images = images.flip((-1))
          width = images.shape[3]
          flipped_boxes[:, [0, 2]] = width - boxes[:, [2, 0]] - 1
  
      return images, flipped_boxes
  
imports: import copy
  import math
  from typing import Tuple
  import numpy
  import torch
label_desc: Use image node_id 12 for calling a global function with example usage: horizontal_flip_with_boxes(prob, images, boxes) and returns: images,  flipped_boxes