code: def prepare_video(
      num_frames,
      num_channels,
      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(num_frames):
          video.append(
              np.random.randint(
                  255,
                  size=(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 pathlib
  import tempfile
  from transformers import BatchFeature
  from transformers.image_utils import AnnotationFormat, AnnotionFormat
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_video(num_frames, num_channels, width, height, numpify, torchify) and returns: video