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","."]
docstr: Returns vocab size
docstr_tok: ["Returns","vocab","size"]
code: def vocab_size(self):
      """Returns vocab size"""
      return self.sp_model.get_piece_size()
  
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 4 for calling the BaiChuanTokenizer obj's underlying member method code with example usage: obj.vocab_size() and returns: self