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 a tokenized string.
docstr_tok: ["Returns","a","tokenized","string","."]
code: def _tokenize(self, text):
      """Returns a tokenized string."""
      return self.sp_model.encode(text, out_type=str)
  
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 6 for calling the BaiChuanTokenizer obj's underlying member method code with example usage: obj._tokenize(text) and returns: self