code: def add_sub_symbol(bert_tokens: List[str], chinese_word_set: set()):
      if not chinese_word_set:
          return bert_tokens
      max_word_len = max([len(w) for w in chinese_word_set])
  
      bert_word = bert_tokens
      start, end = 0, len(bert_word)
      while start < end:
          single_word = True
          if is_chinese(bert_word[start]):
              l = min(end - start, max_word_len)
              for i in range(l, 1, -1):
                  whole_word = "".join(bert_word[start : start + i])
                  if whole_word in chinese_word_set:
                      for j in range(start + 1, start + i):
                          bert_word[j] = "##" + bert_word[j]
                      start = start + i
                      single_word = False
                      break
          if single_word:
              start += 1
      return bert_word
  
file_path: transformers/examples/research_projects/mlm_wwm/run_chinese_ref.py
imports: import argparse
  import json
  from typing import List
  from ltp import LTP
  from transformers.models.bert.tokenization_bert import BertTokenizer
label_desc: Use image node_id 4 for calling a global function with example usage: add_sub_symbol(bert_tokens, chinese_word_set) and returns: bert_word, bert_tokens