code: def convert_vocab(args, converted):
      print("converting vocab...")
      # at this step we don't add <extra_id>s into the vocab, but just save the original binary file directly
      # those special tokens are added only when instantiating a T5Tokenizer
      vocab_path = os.path.join(
          args.dest_dir,
          "mt5.vocab" if "mt5" in args.model_name else "t5.vocab",
      )
      download(url=args.vocab_url, path=vocab_path)
      converted["vocab"] = vocab_path
  
imports: import argparse
  import logging
  import json
  import os
  import shutil
  import tempfile
  import torch
  import mxnet
  from mxnet import np, npx
  from gluonnlp.attention_cell import gen_self_attn_mask, gen_mem_attn_mask
  from gluonnlp.data.tokenizers import SentencepieceTokenizer
  from gluonnlp.models.t5 import T5Model
  from gluonnlp.utils.misc import download, logging_config, sha1sum, naming_convention
  from transformers import T5Model
label_desc: Use image node_id 3 for calling a global function with example usage: convert_vocab(args, converted) without return types