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 as a dict
docstr_tok: ["Returns","vocab","as","a","dict"]
code: def get_vocab(self):
      """Returns vocab as a dict"""
      vocab = {
          self.convert_ids_to_tokens(i): i
          for i in range(self.vocab_size)
      }
      vocab.update(self.added_tokens_encoder)
      return vocab
  
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 5 for calling the BaiChuanTokenizer obj's underlying member method code with example usage: obj.get_vocab() and returns: vocab