code: def __init__(self, model_path: str):
      # reload tokenizer
      assert os.path.isfile(model_path), model_path
      self.sp_model = SentencePieceProcessor(model_file=model_path)
      logger.info(f"Reloaded SentencePiece model from {model_path}")
  
      # BOS / EOS token IDs
      self.n_words: int = self.sp_model.vocab_size()
      self.bos_id: int = self.sp_model.bos_id()
      self.eos_id: int = self.sp_model.eos_id()
      self.pad_id: int = self.sp_model.pad_id()
  
      # token IDs for special infilling tokens
      self.prefix_id: Optional[int] = (
          self.sp_model.piece_to_id("▁<PRE>") or None
      )
      self.middle_id: Optional[int] = (
          self.sp_model.piece_to_id("▁<MID>") or None
      )
      self.suffix_id: Optional[int] = (
          self.sp_model.piece_to_id("▁<SUF>") or None
      )
      self.eot_id: Optional[int] = (
          self.sp_model.piece_to_id("▁<EOT>") or None
      )
      logger.info(
          f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id} "
          f"- PRE ID: {self.prefix_id} - MID ID: {self.middle_id} - SUF ID: {self.suffix_id} - EOT ID: {self.eot_id}"
      )
      assert (
          self.sp_model.vocab_size() == self.sp_model.get_piece_size()
      )
  
file_path: codellama/llama/tokenizer.py
imports: import os
  from logging import getLogger
  from typing import List, Optional
  from sentencepiece import SentencePieceProcessor
label_desc: Use image node_id 1 to create a new Tokenizer object  with example: obj = Tokenizer(model_path)