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: Converts an index (integer) in a token (str) using the vocab.
docstr_tok: ["Converts","an","index","(","integer",")","in","a","token","(","str",")","using","the","vocab","."]
code: def _convert_id_to_token(self, index):
      """Converts an index (integer) in a token (str) using the vocab."""
      token = self.sp_model.IdToPiece(index)
      return token
  
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 8 for calling the BaiChuanTokenizer obj's underlying member method code with example usage: obj._convert_id_to_token(index) and returns: token