class_docstr: Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models.
class_docstr_tok: ["Runs","pre-tokenization","with","Jieba","segmentation","tool",".","It","is","used","in","CPM","models","."]
docstr: Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
  special tokens using the tokenizer `prepare_for_model` method.
  
  Args:
      token_ids_0 (`List[int]`):
          List of IDs.
      token_ids_1 (`List[int]`, *optional*):
          Optional second list of IDs for sequence pairs.
      already_has_special_tokens (`bool`, *optional*, defaults to `False`):
          Whether or not the token list is already formatted with special tokens for the model.
  
  Returns:
      `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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code: def get_special_tokens_mask(
      self,
      token_ids_0: List[int],
      token_ids_1: Optional[List[int]] = None,
      already_has_special_tokens: bool = False,
  ) -> List[int]:
      """
      Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
      special tokens using the tokenizer `prepare_for_model` method.
  
      Args:
          token_ids_0 (`List[int]`):
              List of IDs.
          token_ids_1 (`List[int]`, *optional*):
              Optional second list of IDs for sequence pairs.
          already_has_special_tokens (`bool`, *optional*, defaults to `False`):
              Whether or not the token list is already formatted with special tokens for the model.
  
      Returns:
          `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
      """
  
      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_0=token_ids_0,
              token_ids_1=token_ids_1,
              already_has_special_tokens=True,
          )
  
      if token_ids_1 is not None:
          return (
              ([0] * len(token_ids_0))
              + [1]
              + ([0] * len(token_ids_1))
              + [1, 1]
          )
      return ([0] * len(token_ids_0)) + [1, 1]
  
imports: import os
  import unicodedata
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import SPIECE_UNDERLINE, logging
label_desc: Use image node_id 12 for calling the CpmTokenizer obj's underlying member method code with example usage: obj.get_special_tokens_mask(token_ids_0, token_ids_1, already_has_special_tokens) and returns: unknown, super, unknown