docstr: 在空白和一些标点符号（如逗号或句点）上拆分文本
  Arg :
      text (String) : 所需拆分文本
  Return :
      tokens (List) : 分词完的结果
docstr_tok: ["\u5728\u7a7a\u767d\u548c\u4e00\u4e9b\u6807\u70b9\u7b26\u53f7\uff08\u5982\u9017\u53f7\u6216\u53e5\u70b9\uff09\u4e0a\u62c6\u5206\u6587\u672c","Arg",":","text","(","String",")",":","\u6240\u9700\u62c6\u5206\u6587\u672c","Return",":","tokens","(","List",")",":","\u5206\u8bcd\u5b8c\u7684\u7ed3\u679c"]
code: def _orig_tokenize(self, text):
      """
      在空白和一些标点符号（如逗号或句点）上拆分文本
      Arg :
          text (String) : 所需拆分文本
      Return :
          tokens (List) : 分词完的结果
      """
      text = text.strip()
      if not text:
          return []
      # 常见的断句标点
      punc = """,.?!;: 、｜，。？！；：《》「」【】/<>|\“ ”‘ ’"""
      punc_re = "|".join(re.escape(x) for x in punc)
      tokens = re.sub(punc_re, lambda x: " " + x.group() + " ", text)
      tokens = tokens.split()
      return tokens
  
file_path: DeepKE/src/deepke/attribution_extraction/standard/tools/serializer.py
imports: import re
  import unicodedata
  import jieba
  import logging
  from typing import List
label_desc: Use image node_id 6 for calling the Serializer obj's underlying member method code with example usage: obj._orig_tokenize(text) and returns: tokens, list