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 __init__(
      self,
      max_seq_length: int,
      tokenizer: PreTrainedTokenizer,
      truncate_method: Optional[str] = "tail",
      predict_eos_token: Optional[bool] = False,
      **kwargs,
  ):
      super().__init__(
          max_seq_length=max_seq_length,
          tokenizer=tokenizer,
          truncate_method=truncate_method,
      )
      self.predict_eos = predict_eos_token
  
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 1 to create a new LMTokenizerWrapper object from inherited base classes: TokenizerWrapper with example: obj = LMTokenizerWrapper(max_seq_length, tokenizer, truncate_method, predict_eos_token)