code: def main():
      """
      Combine the two datasets, parse them, and write out the results
      """
      args = parse_args()
  
      foundation_cache = FoundationCache()
      ssplit_pipe = selftrain.build_ssplit_pipe(
          ssplit=True, lang=args.lang
      )
      tag_pipe = selftrain.build_tag_pipe(
          ssplit=False,
          lang=args.lang,
          foundation_cache=foundation_cache,
      )
      parser_pipes = selftrain.build_parser_pipes(
          args.lang,
          args.models,
          package=args.package,
          foundation_cache=foundation_cache,
      )
  
      docs = get_paccss(args.input_dir)
      if args.europarl:
          docs.extend(get_europarl(args.input_dir, ssplit_pipe))
  
      logger.info("Processing %d docs", len(docs))
      new_trees = selftrain.find_matching_trees(
          docs,
          args.num_sentences,
          set(),
          tag_pipe,
          parser_pipes,
          shuffle=False,
          chunk_size=100,
          output_ptb=args.output_ptb,
      )
      logger.info(
          "Found %d unique trees which are the same between models"
          % len(new_trees)
      )
      with open(args.output_file, "w") as fout:
          for tree in sorted(new_trees):
              fout.write(tree)
              fout.write("\n")
  
file_path: stanza/stanza/utils/datasets/constituency/selftrain_it.py
imports: import argparse
  import logging
  import os
  import random
  import stanza
  from stanza.models.common.foundation_cache import FoundationCache
  from stanza.utils.datasets.constituency import selftrain
  from stanza.utils.get_tqdm import get_tqdm
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types