code: def print_results(cfg, result_name, corpuses, algorithms):
      result_table = [
          ["#" for _ in range(len(corpuses) + 1)]
          for _ in range(len(algorithms))
      ]
      table_header = ["#"] + [lang for lang in corpuses]
      rev_lang = {lang: i for i, lang in enumerate(table_header)}
      rev_algo = {algo: i for i, algo in enumerate(algorithms)}
      for i, algo_name in enumerate(algorithms):
          result_table[i][0] = algo_name
  
      for lang, res in cfg.items():
          best = min(res.values())
          for algo in res:
              j = rev_lang[lang]
              i = rev_algo[algo]
              multiplier_str = f"{res[algo]/best:.1f}".rstrip(
                  "0"
              ).rstrip(".")
              result_table[i][
                  j
              ] = f"{res[algo]:.1f} (x{multiplier_str})"
  
      table_header[0] = result_name
      column_align = ["left"] + ["center" for _ in corpuses]
      print(
          tabulate(
              result_table,
              table_header,
              tablefmt="github",
              colalign=column_align,
          )
      )
  
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 7 for calling a global function with example usage: print_results(cfg, result_name, corpuses, algorithms) without return types