code: def create_dataset(
      image_folder: str, annotation_folder: str, output_path: str
  ):
      """
      Read the training set of KITTI and split it into a training split and a validation split
      which follows the disk_folder format of VISSL
      """
      dataset = KITTIDistance(
          image_folder, annotation_folder, output_path
      )
      for split in ["train", "val"]:
          for target in KITTIDistance.LABELS:
              os.makedirs(
                  os.path.join(output_path, split, target),
                  exist_ok=True,
              )
  
      loader = DataLoader(
          dataset,
          num_workers=8,
          batch_size=1,
          collate_fn=lambda x: x[0],
      )
      with tqdm(total=len(dataset)) as progress_bar:
          for _ in loader:
              progress_bar.update(1)
  
file_path: vissl/extra_scripts/datasets/create_kitti_dist_data_files.py
imports: import argparse
  import bisect
  import math
  import os
  import shutil
  from typing import Set
  import numpy
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from vissl.utils.download import download_and_extract_archive
label_desc: Use image node_id 3 for calling a global function with example usage: create_dataset(image_folder, annotation_folder, output_path) without return types