code: def convert_mt5(args):
      logging.info("converting mT5 model from Huggingface...")
      if not os.path.exists(args.dest_dir):
          os.mkdir(args.dest_dir)
      converted = {}
      # convert and save vocab
      convert_vocab(args, converted)
      # convert and save config
      gluon_cfg = Gluon_mT5.get_cfg(args.tgt_model_name)
      gluon_cfg = convert_config(args, gluon_cfg, converted)
      # convert, (test), and save model
      hf_mt5 = HF_mT5.from_pretrained(args.model_name)
      gluon_mt5 = Gluon_mT5.from_cfg(gluon_cfg)
      gluon_mt5 = convert_params(args, converted, hf_mt5, gluon_mt5)
      gluon_mt5.hybridize()
      # test model if needed
      if args.test:
          test_conversion(args, hf_mt5, gluon_mt5)
      # rename with sha1sum
      rename(args, converted)
      logging.info("conversion completed.")
      logging.info("file statistics:")
      for item, new_path in converted.items():
          logging.info(
              "filename: {}\tsize: {}\tsha1sum: {}".format(
                  os.path.basename(new_path),
                  os.path.getsize(new_path),
                  sha1sum(new_path),
              )
          )
      return converted
  
file_path: gluon-nlp/scripts/conversion_toolkits/convert_mt5.py
imports: import argparse
  import logging
  import os
  from gluonnlp.utils.misc import logging_config, sha1sum
  from gluonnlp.models.mt5 import MT5Model
  from transformers import MT5Model
  from convert_t5 import parse_args, convert_config, convert_vocab, convert_params, rename, test_conversion
label_desc: Use image node_id 2 for calling a global function with example usage: convert_mt5(args) and returns: converted