code: def __iter__(self):
      for i in range(self.epoch_size):
          # Get random sample
          path, target = random.choice(self.samples)
          # Get video object
          vid = torchvision.io.VideoReader(path, "video")
          metadata = vid.get_metadata()
          video_frames = []  # video frame buffer
  
          # Seek and return frames
          max_seek = metadata["video"]["duration"][0] - (
              self.clip_len / metadata["video"]["fps"][0]
          )
          start = random.uniform(0.0, max_seek)
          for frame in itertools.islice(vid.seek(start), self.clip_len):
              video_frames.append(self.frame_transform(frame["data"]))
              current_pts = frame["pts"]
          # Stack it into a tensor
          video = torch.stack(video_frames, 0)
          if self.video_transform:
              video = self.video_transform(video)
          output = {
              "path": path,
              "video": video,
              "target": target,
              "start": start,
              "end": current_pts,
          }
          yield output
  
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 2 for calling the RandomDataset obj's underlying member method code with example usage: obj.__iter__() without return types