code: def main(argv: List[str]) -> None:
      """
      This function processes the sparse features (.npy) to be contiguous
      and saves the result in a separate (.npy) file.
  
      Args:
          argv (List[str]): Command line args.
  
      Returns:
          None.
      """
  
      args = parse_args(argv)
      input_dir = args.input_dir
      output_dir = args.output_dir
  
      # Look for files that end in "_sparse.npy" since this processing is
      # only applied to sparse data.
  
      input_files = [
          os.path.join(input_dir, f"day_{i}_sparse.npy")
          for i in range(DAYS)
      ]
  
      if not input_files:
          raise ValueError(
              f"There are no files that end with '_sparse.npy' in this directory: {input_dir}"
          )
  
      print(
          f"Processing files in: {input_files}. Outputs will be saved to {output_dir}."
      )
      BinaryCriteoUtils.sparse_to_contiguous(
          input_files,
          output_dir,
          frequency_threshold=int(args.frequency_threshold),
      )
      print("Done processing.")
  
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