code: def __init__(
      self,
      root,
      epoch_size=None,
      frame_transform=None,
      video_transform=None,
      clip_len=16,
  ):
      super(RandomDataset).__init__()
  
      self.samples = get_samples(root)
  
      # Allow for temporal jittering
      if epoch_size is None:
          epoch_size = len(self.samples)
      self.epoch_size = epoch_size
  
      self.clip_len = clip_len
      self.frame_transform = frame_transform
      self.video_transform = video_transform
  
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 to create a new RandomDataset object from inherited base classes: torch.utils.data with example: obj = RandomDataset(root, epoch_size, frame_transform, video_transform, clip_len)