docstr: This need to be specified in specific language model
          
docstr_tok: ["This","need","to","be","specified","in","specific","language","model"]
code: def special_tokens_maps(
      self,
  ) -> Dict:
      r"""This need to be specified in specific language model"""
      if not hasattr(self, "_special_tokens_map"):
          _special_tokens_map = {}
          for attrname in self.__dict__.keys():
              if attrname.endswith("_token_map"):
                  _special_tokens_map.update(getattr(self, attrname))
      return _special_tokens_map
  
imports: import itertools
  import warnings
  import torch
  import torch.nn
  from torch.utils.data import Dataset
  from typing import Union, List, Tuple, Dict, Optional
  from transformers.data.processors.utils import InputExample, InputFeatures
  from transformers.tokenization_utils import PreTrainedTokenizer
  from collections import defaultdict
  from openprompt.utils import round_list
  import numpy
label_desc: Use image node_id 3 for calling the TokenizerWrapper obj's underlying member method code with example usage: obj.special_tokens_maps() and returns: _special_tokens_map