code: def main(args):
      langs = (
          args.langs if isinstance(args.langs, list) else [args.langs]
      )
      # Hugging Face - limit number of processes
      os.environ["RAYON_RS_NUM_CPUS"] = str(args.n_threads)
  
      short_to_long_names = {
          "en": "English",
          "ru": "Russian",
          "ja": "Japanese",
          "zh": "Chinese",
      }
  
      # For adding more languages check out this page https://linguatools.org/tools/corpora/wikipedia-monolingual-corpora/
      all_links = {
          "English": "https://www.dropbox.com/s/cnrhd11zdtc1pic/enwiki-20181001-corpus.xml.bz2?dl=1",
          "Russian": "https://www.dropbox.com/s/lpfmyrl7nxn5ugg/ruwiki-20181001-corpus.xml.bz2?dl=1",
          "Japanese": "https://www.dropbox.com/s/wf496hlu512z9kc/jawiki-20140807-corpus.xml.bz2?dl=1",
          "Chinese": "https://www.dropbox.com/s/czhr6s5jwaljeue/zhwiki-20140804-corpus.xml.bz2?dl=1",
      }
      links = {
          short_to_long_names[lang]: all_links[
              short_to_long_names[lang]
          ]
          for lang in langs
      }
  
      corpuses = {}
      Path("data").mkdir(exist_ok=True)
      for lang, link in links.items():
          Path(f"data/{lang}").mkdir(exist_ok=True)
          zip_file = f"data/{lang}/wiki.xml.bz2"
          if not Path(zip_file).exists():
              os.system(f"wget -O {zip_file} {link}")
          corpuses[lang] = prepare_data(zip_file, args.corpus_size)
  
      algorithms = [
          YOU_TOKEN_TO_ME,
          HUGGING_FACE_BPE,
          SENTENCE_PIECE,
          FAST_BPE,
      ]
  
      global_train = {}
      global_tokenization = {}
  
      for lang, corpus_path in corpuses.items():
          train_stat, tokenization_stat = speed_test(
              corpus_path, args.vocab_size, algorithms, args.n_threads
          )
          global_train[lang] = train_stat
          global_tokenization[lang] = tokenization_stat
  
      print_results(
          global_train,
          f"Train {args.corpus_size}MB",
          corpuses,
          algorithms,
      )
      print_results(
          global_tokenization,
          f"Tokenization {args.corpus_size}MB",
          corpuses,
          algorithms,
      )
  
imports: import argparse
  import os
  from pathlib import Path
  from time import time
  from tabulate import tabulate
  from tokenizers import pre_tokenizers
  from tokenizers import Tokenizer
  from tokenizers.models import BPE
  from tokenizers.trainers import BpeTrainer
label_desc: Use image node_id 9 for calling a global function with example usage: main(args) without return types