code: def create_small_kinetics_dataset(root_dir: str) -> None:
      """
      A test utility function to create a small Kinetics like dataset
  
      Args:
          root_dir(str): The directory to create the dataset in.
          Typically, a temporary directory is used.
      """
      video_codec = "libx264rgb"
      options = {"crf": "0"}
      height: int = 250
      width: int = 250
      num_frames = 20
      fps = 5
      data = create_dummy_video_frames(num_frames, height, width)
  
      train_data = [
          ["a.mp4", "308"],
          ["b.mp4", "298"],
          ["c.mp4", "240"],
          ["d.mp4", "363"],
      ]
  
      val_data = [
          ["a.mp4", "151"],
      ]
  
      for i in range(4):
          io.write_video(
              os.path.join(root_dir, train_data[i][0]),
              data,
              fps=fps,
              video_codec=video_codec,
              options=options,
          )
  
      train_file = os.path.join(root_dir, "train.csv")
      write_single_csv_file(train_file, train_data)
  
      val_file = os.path.join(root_dir, "val.csv")
      write_single_csv_file(val_file, val_data)
  
imports: import csv
  import os
  from functools import wraps
  from tempfile import TemporaryDirectory
  from typing import Any, Callable, Dict, List, Optional
  from unittest import mock
  import testslide
  import torch
  import torchvision.io
  from hydra import compose, initialize_config_module
  from hydra.utils import instantiate
  from omegaconf import OmegaConf
  from torchrecipes.core.base_train_app import BaseTrainApp, TrainOutput
label_desc: Use image node_id 1 for calling a global function with example usage: create_small_kinetics_dataset(root_dir) without return types