class_docstr: Encoder decoder models with Byte Pair Encoding and various lattice losses.
class_docstr_tok: ["Encoder","decoder","models","with","Byte","Pair","Encoding","and","various","lattice","losses","."]
docstr: Changes vocabulary of the tokenizer used during CTC decoding process.
  Use this method when fine-tuning on from pre-trained model.
  This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would
  use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need
  model to learn capitalization, punctuation and/or special characters.
  
  Args:
      new_tokenizer_dir: Path to the new tokenizer directory.
      new_tokenizer_type: Either `bpe` or `wpe`. `bpe` is used for SentencePiece tokenizers,
          whereas `wpe` is used for `BertTokenizer`.
  
  Returns: None
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code: def change_vocabulary(
      self, new_tokenizer_dir: str, new_tokenizer_type: str
  ):
      """
      Changes vocabulary of the tokenizer used during CTC decoding process.
      Use this method when fine-tuning on from pre-trained model.
      This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would
      use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need
      model to learn capitalization, punctuation and/or special characters.
  
      Args:
          new_tokenizer_dir: Path to the new tokenizer directory.
          new_tokenizer_type: Either `bpe` or `wpe`. `bpe` is used for SentencePiece tokenizers,
              whereas `wpe` is used for `BertTokenizer`.
  
      Returns: None
  
      """
      super().change_vocabulary(new_tokenizer_dir, new_tokenizer_type)
  
      if self.use_graph_lm:
          self.token_lm = None
          logging.warning(
              f"""With .change_vocabulary() call for a model with criterion_type=`{self.loss.criterion_type}`, 
                  a new token_lm has to be set manually: call .update_k2_modules(new_cfg) 
                  or update .graph_module_cfg.backend_cfg.token_lm before calling this method."""
          )
  
      self.update_k2_modules(self.graph_module_cfg)
  
imports: from typing import List, Optional
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE
  from nemo.collections.asr.models.ctc_models import EncDecCTCModel
  from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel
  from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel
  from nemo.collections.asr.parts.k2.classes import ASRK2Mixin
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the EncDecK2SeqModelBPE obj's underlying member method code with example usage: obj.change_vocabulary(new_tokenizer_dir, new_tokenizer_type) without return types