code: def main():
      args = parse_args()
  
      random.seed(1234)
  
      wiki_files = list_wikipedia_files(args.input_dir)
      if args.shuffle:
          random.shuffle(wiki_files)
  
      foundation_cache = FoundationCache()
      tag_pipe = selftrain.build_tag_pipe(
          ssplit=True, lang=args.lang, foundation_cache=foundation_cache
      )
      parser_pipes = selftrain.build_parser_pipes(
          args.lang, args.models, foundation_cache=foundation_cache
      )
  
      # create a blank file.  we will append to this file so that partial results can be used
      with open(args.output_file, "w") as fout:
          pass
  
      accepted_trees = set()
      for filename in tqdm(wiki_files, disable=False):
          docs = read_wiki_file(filename)
          new_trees = selftrain.find_matching_trees(
              docs,
              args.num_sentences,
              accepted_trees,
              tag_pipe,
              parser_pipes,
              shuffle=args.shuffle,
          )
          accepted_trees.update(new_trees)
  
          with open(args.output_file, "a") as fout:
              for tree in sorted(new_trees):
                  fout.write(tree)
                  fout.write("\n")
  
file_path: stanza/stanza/utils/datasets/constituency/selftrain_wiki.py
imports: import argparse
  from collections import deque
  import glob
  import os
  import random
  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