code: def update_tokenizer(asr_model, tokenizer_dir, tokenizer_type):
      """
      Updates the tokenizer of the model and also reinitializes the decoder if the vocabulary size
      of the new tokenizer differs from that of the loaded model.
      Args:
          asr_model: ASRModel instance
          tokenizer_dir: tokenizer directory
          tokenizer_type: tokenizer type
      Returns:
          asr_model: ASRModel instance with updated tokenizer and decoder
      """
      vocab_size = asr_model.tokenizer.vocab_size
      decoder = asr_model.decoder.state_dict()
      if hasattr(asr_model, "joint"):
          joint_state = asr_model.joint.state_dict()
      else:
          joint_state = None
  
      if tokenizer_dir is None:
          raise ValueError(
              "dir must be specified if update_tokenizer is True"
          )
      logging.info("Using the tokenizer provided through config")
      asr_model.change_vocabulary(
          new_tokenizer_dir=tokenizer_dir,
          new_tokenizer_type=tokenizer_type,
      )
      if asr_model.tokenizer.vocab_size != vocab_size:
          logging.warning(
              "The vocabulary size of the new tokenizer differs from that of the loaded model. As a result, finetuning will proceed with the new vocabulary, and the decoder will be reinitialized."
          )
      else:
          asr_model.decoder.load_state_dict(decoder)
          if joint_state is not None:
              asr_model.joint.load_state_dict(joint_state)
  
      return asr_model
  
file_path: NeMo/examples/asr/speech_to_text_finetune.py
imports: import time
  import pytorch_lightning
  from omegaconf import OmegaConf
  from nemo.collections.asr.models import ASRModel
  from nemo.core.config import hydra_runner
  from nemo.utils import logging, model_utils
  from nemo.utils.exp_manager import exp_manager
  from nemo.utils.get_rank import is_global_rank_zero
label_desc: Use image node_id 3 for calling a global function with example usage: update_tokenizer(asr_model, tokenizer_dir, tokenizer_type) and returns: asr_model