class_docstr: LMTokenizer is a causual language model. Therefore it can only predict <mask> position
  at the end of the sentence. A prefix-style template like: 'A <mask> news : <text_a> <text_b> ' is
  not applicable in this situation.
  For the template where there is '<text_a>' or '<text_b>' after '<mask>', we raise an exception and terminate
  the program.
  For the template where there are template words after '<mask>', we ignore these template words.
  Moreover, it can only predict one '<mask>' position. All template that has multiple '<mask>' will
  give rise to an exception.
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code: def num_special_tokens_to_add(self):
      if not hasattr(self, "_num_specials"):
          self._num_specials = (
              self.tokenizer.num_special_tokens_to_add()
          )
      return self._num_specials
  
file_path: OpenPrompt/openprompt/plms/lm.py
imports: from transformers.tokenization_utils import PreTrainedTokenizer
  from openprompt.plms.utils import TokenizerWrapper
  from typing import List, Dict, Optional
  from collections import defaultdict
  import numpy
  from openprompt.utils.logging import logger
label_desc: Use image node_id 2 for calling the LMTokenizerWrapper obj's underlying member method code with example usage: obj.num_special_tokens_to_add() and returns: self