code: def convert_all_sentencepiece_models(
      model_list=None, repo_path=None, dest_dir=Path("marian_converted")
  ):
      """Requires 300GB"""
      save_dir = Path("marian_ckpt")
      dest_dir = Path(dest_dir)
      dest_dir.mkdir(exist_ok=True)
      save_paths = []
      if model_list is None:
          model_list: list = make_registry(repo_path=repo_path)
      for k, prepro, download, test_set_url in tqdm(model_list):
          if "SentencePiece" not in prepro:  # dont convert BPE models.
              continue
          if not os.path.exists(save_dir / k):
              download_and_unzip(download, save_dir / k)
          pair_name = convert_opus_name_to_hf_name(k)
          convert(save_dir / k, dest_dir / f"opus-mt-{pair_name}")
  
          save_paths.append(dest_dir / f"opus-mt-{pair_name}")
      return save_paths
  
imports: import argparse
  import json
  import os
  import socket
  import time
  import warnings
  from pathlib import Path
  from typing import Dict, List, Union
  from zipfile import ZipFile
  import numpy
  import torch
  from huggingface_hub.hf_api import list_models
  from torch import nn
  from tqdm import tqdm
  from transformers import MarianConfig, MarianMTModel, MarianTokenizer
label_desc: Use image node_id 16 for calling a global function with example usage: convert_all_sentencepiece_models(model_list, repo_path, dest_dir) and returns: save_paths