code: def prepare_video(
      feature_extract_tester,
      width=10,
      height=10,
      numpify=False,
      torchify=False,
  ):
      """This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
  
      video = []
      for i in range(feature_extract_tester.num_frames):
          video.append(
              np.random.randint(
                  255,
                  size=(
                      feature_extract_tester.num_channels,
                      width,
                      height,
                  ),
                  dtype=np.uint8,
              )
          )
  
      if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          video = [
              Image.fromarray(np.moveaxis(frame, 0, -1))
              for frame in video
          ]
  
      if torchify:
          video = [torch.from_numpy(frame) for frame in video]
  
      return video
  
imports: import json
  import os
  import sys
  import tempfile
  import unittest
  import unittest.mock
  from pathlib import Path
  from huggingface_hub import HfFolder, delete_repo, set_access_token
  from requests.exceptions import HTTPError
  from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
  from transformers.testing_utils import TOKEN, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test
  from transformers.utils import is_torch_available, is_vision_available
  from test_module.custom_feature_extraction import CustomFeatureExtractor
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_video(feature_extract_tester, width, height, numpify, torchify) and returns: video