code: def convert_tf_assets(tf_assets_dir, model_size, electra_path):
      """Convert the assets file including config, vocab and tokenizer model"""
      file_names = os.listdir(tf_assets_dir)
      vocab_path = None
      for ele in file_names:
          if ele.endswith(".txt"):
              assert vocab_path is None
              vocab_path = ele
      assert vocab_path is not None
  
      if vocab_path:
          vocab_path = os.path.join(tf_assets_dir, vocab_path)
          vocab_size = len(
              open(vocab_path, "r", encoding="utf-8").readlines()
          )
      config_dict = get_dict_config(model_size, electra_path)
      cfg = convert_tf_config(config_dict, vocab_size)
      return cfg, vocab_path
  
imports: import os
  import re
  import sys
  import shutil
  import logging
  import argparse
  import mxnet
  import numpy
  from numpy.testing import assert_allclose
  from gluonnlp.utils.misc import naming_convention, logging_config
  from gluonnlp.data.tokenizers import HuggingFaceWordPieceTokenizer
  from gluonnlp.models.electra import ElectraModel, ElectraGenerator, ElectraDiscriminator, ElectraForPretrain, get_generator_cfg
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling a global function with example usage: convert_tf_assets(tf_assets_dir, model_size, electra_path) and returns: cfg,  vocab_path