class_docstr: Construct a BaiChuan tokenizer. Based on byte-level Byte-Pair-Encoding.
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
class_docstr_tok: ["Construct","a","BaiChuan","tokenizer",".","Based","on","byte-level","Byte-Pair-Encoding",".","Args",":","vocab_file","(","`","str","`",")",":","Path","to","the","vocabulary","file","."]
code: def build_inputs_with_special_tokens(
      self, token_ids_0, token_ids_1=None
  ):
      bos_token_id = [self.bos_token_id] if self.add_bos_token else []
      eos_token_id = [self.eos_token_id] if self.add_eos_token else []
  
      output = bos_token_id + token_ids_0 + eos_token_id
  
      if token_ids_1 is not None:
          output = output + bos_token_id + token_ids_1 + eos_token_id
  
      return output
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
  from transformers.utils import logging
label_desc: Use image node_id 11 for calling the BaiChuanTokenizer obj's underlying member method code with example usage: obj.build_inputs_with_special_tokens(token_ids_0, token_ids_1) and returns: output