class_docstr: Convert Tatoeba-Challenge models to huggingface format.
  
  Steps:
  
      1. Convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
      2. Rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique
         one exists. e.g. aav-eng -> aav-en, heb-eng -> he-en
      3. Select the best model for a particular pair, parse the yml for it and write a model card. By default the
         best model is the one listed first in released-model-results, but it's also possible to specify the most
         recent one.
code: def download_lang_info(self):
      Path(LANG_CODE_PATH).parent.mkdir(exist_ok=True)
      import wget
  
      if not os.path.exists(ISO_PATH):
          wget.download(ISO_URL, ISO_PATH)
      if not os.path.exists(LANG_CODE_PATH):
          wget.download(LANG_CODE_URL, LANG_CODE_PATH)
  
imports: import argparse
  import datetime
  import json
  import os
  import re
  from pathlib import Path
  from typing import Tuple
  from tqdm import tqdm
  import yaml
  from transformers.models.marian.convert_marian_to_pytorch import FRONT_MATTER_TEMPLATE, convert, convert_opus_name_to_hf_name, download_and_unzip, get_system_metadata
label_desc: Use image node_id 9 for calling the TatoebaConverter obj's underlying member method code with example usage: obj.download_lang_info() without return types