class_docstr: Dataset used to parallelize the transformation of the dataset via a DataLoader
class_docstr_tok: ["Dataset","used","to","parallelize","the","transformation","of","the","dataset","via","a","DataLoader"]
code: def __getitem__(self, idx: int) -> Tuple[Image.Image, str, str, str]:
      video_path = self.video_paths[idx]
      label = self.video_labels[idx]
      mid_frame = self._extract_middle_frame(video_path)
      video_name = os.path.split(video_path)[1]
      image_name = os.path.splitext(video_name)[0] + ".jpg"
      return mid_frame, image_name, label, video_path
  
file_path: vissl/extra_scripts/datasets/create_k700_data_files.py
imports: import argparse
  import os
  from typing import Optional, Tuple
  import numpy
  from PIL import Image
  from torch.utils.data import DataLoader
  from torchvision.datasets.utils import download_url
  from tqdm import tqdm
  from vissl.utils.download import download_and_extract_archive
  from vissl.utils.io import save_file
label_desc: Use image node_id 5 for calling the KineticsMiddleFrameDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: mid_frame,  image_name,  label,  video_path