code: def crop_boxes(boxes, x_offset, y_offset):
      """
      Perform crop on the bounding boxes given the offsets.
      Args:
          boxes (ndarray or None): bounding boxes to perform crop. The dimension
              is `num boxes` x 4.
          x_offset (int): cropping offset in the x axis.
          y_offset (int): cropping offset in the y axis.
      Returns:
          cropped_boxes (ndarray or None): the cropped boxes with dimension of
              `num boxes` x 4.
      """
      cropped_boxes = boxes.copy()
      cropped_boxes[:, [0, 2]] = boxes[:, [0, 2]] - x_offset
      cropped_boxes[:, [1, 3]] = boxes[:, [1, 3]] - y_offset
  
      return cropped_boxes
  
imports: import logging
  import math
  import torch
  import torch.nn
  import torchaudio
  from PIL import Image
  from pytorchvideo import transforms
  from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler
  from pytorchvideo.data.encoded_video import EncodedVideo
  from torchvision import transforms
  from torchvision.transforms._transforms_video import NormalizeVideo
  from imagebind.models.multimodal_preprocessors import SimpleTokenizer
label_desc: Use image node_id 6 for calling a global function with example usage: crop_boxes(boxes, x_offset, y_offset) and returns: cropped_boxes