code: def main(argv: List[str]) -> None:
      """
      This function preprocesses the raw Criteo tsvs into the format (npy binary)
      expected by InMemoryBinaryCriteoIterDataPipe.
  
      Args:
          argv (List[str]): Command line args.
  
      Returns:
          None.
      """
  
      args = parse_args(argv)
      input_dir = args.input_dir
      output_dir = args.output_dir
  
      if args.dataset_name == "criteo_1tb":
          in_files_l = [f"day_{i}" for i in range(24)]
          out_files_l = in_files_l
      else:
          # criteo_kaggle code path
          in_files_l = ["train.txt", "test.txt"]
          out_files_l = ["train", "test"]
  
      for input, output in zip(in_files_l, out_files_l):
          in_file_path = os.path.join(input_dir, input)
          if not os.path.exists(in_file_path):
              continue
          dense_out_file_path = os.path.join(
              output_dir, output + "_dense.npy"
          )
          sparse_out_file_path = os.path.join(
              output_dir, output + "_sparse.npy"
          )
          labels_out_file_path = os.path.join(
              output_dir, output + "_labels.npy"
          )
          print(
              f"Processing {in_file_path}.\nOutput will be saved to\n{dense_out_file_path}"
              f"\n{sparse_out_file_path}\n{labels_out_file_path}"
          )
          BinaryCriteoUtils.tsv_to_npys(
              in_file_path,
              dense_out_file_path,
              sparse_out_file_path,
              labels_out_file_path,
              args.dataset_name,
          )
          print(f"Done processing {in_file_path}.")
  
imports: import argparse
  import os
  import sys
  from typing import List
  from torchrec.datasets.criteo import BinaryCriteoUtils
label_desc: Use image node_id 2 for calling a global function with example usage: main(argv) without return types