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: Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet
  sequence pair mask has the following format:
  
  ```
  0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
  | first sequence    | second sequence |
  ```
  
  If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
  
  Args:
      token_ids_0 (`List[int]`):
          List of IDs.
      token_ids_1 (`List[int]`, *optional*):
          Optional second list of IDs for sequence pairs.
  
  Returns:
      `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
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code: def create_token_type_ids_from_sequences(
      self,
      token_ids_0: List[int],
      token_ids_1: Optional[List[int]] = None,
  ) -> List[int]:
      """
      Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet
      sequence pair mask has the following format:
  
      ```
      0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
      | first sequence    | second sequence |
      ```
  
      If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
  
      Args:
          token_ids_0 (`List[int]`):
              List of IDs.
          token_ids_1 (`List[int]`, *optional*):
              Optional second list of IDs for sequence pairs.
  
      Returns:
          `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
      """
      sep = [self.sep_token_id]
      cls_segment_id = [2]
  
      if token_ids_1 is None:
          return len(token_ids_0 + sep) * [0] + cls_segment_id
      return (
          len(token_ids_0 + sep) * [0]
          + len(token_ids_1 + sep) * [1]
          + cls_segment_id
      )
  
file_path: transformers/src/transformers/models/cpm/tokenization_cpm_fast.py
imports: import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
label_desc: Use image node_id 4 for calling the CpmTokenizerFast obj's underlying member method code with example usage: obj.create_token_type_ids_from_sequences(token_ids_0, token_ids_1) and returns: unknown, unknown