code: def example_read_video(
      video_object, start=0, end=None, read_video=True, read_audio=True
  ):
      if end is None:
          end = float("inf")
      if end < start:
          raise ValueError(
              "end time should be larger than start time, got "
              f"start time={start} and end time={end}"
          )
  
      video_frames = torch.empty(0)
      video_pts = []
      if read_video:
          video_object.set_current_stream("video")
          frames = []
          for frame in itertools.takewhile(
              lambda x: x["pts"] <= end, video_object.seek(start)
          ):
              frames.append(frame["data"])
              video_pts.append(frame["pts"])
          if len(frames) > 0:
              video_frames = torch.stack(frames, 0)
  
      audio_frames = torch.empty(0)
      audio_pts = []
      if read_audio:
          video_object.set_current_stream("audio")
          frames = []
          for frame in itertools.takewhile(
              lambda x: x["pts"] <= end, video_object.seek(start)
          ):
              frames.append(frame["data"])
              audio_pts.append(frame["pts"])
          if len(frames) > 0:
              audio_frames = torch.cat(frames, 0)
  
      return (
          video_frames,
          audio_frames,
          (video_pts, audio_pts),
          video_object.get_metadata(),
      )
  
imports: import torch
  import torchvision
  from torchvision.datasets.utils import download_url
  import itertools
  import os
  from torchvision.datasets.utils import download_url
  import os
  import random
  from torchvision.datasets.folder import make_dataset
  from torchvision import transforms
  from torch.utils.data import DataLoader
  import matplotlib.pyplot
  import os
  import shutil
label_desc: Use image node_id 1 for calling a global function with example usage: example_read_video(video_object, start, end, read_video, read_audio) and returns: video_frames,  audio_frames,  video_pts,  audio_pts,  video_object