code: def __init__(self, sp_model):
      super().__init__()
  
      self.sp_model = sp_model
      spm_vocab_size = self.sp_model.get_piece_size()
      assert spm_vocab_size == _expected_spm_vocab_size, (
          "The model returned by conformer_rnnt_base expects a SentencePiece model of "
          f"vocabulary size {_expected_spm_vocab_size}, but the given SentencePiece model has a vocabulary size "
          f"of {spm_vocab_size}. Please provide a correctly configured SentencePiece model."
      )
      self.blank_idx = spm_vocab_size
  
      # ``conformer_rnnt_base`` hardcodes a specific Conformer RNN-T configuration.
      # For greater customizability, please refer to ``conformer_rnnt_model``.
      self.model = conformer_rnnt_base()
      self.loss = torchaudio.transforms.RNNTLoss(reduction="sum")
      self.optimizer = torch.optim.Adam(
          self.model.parameters(), lr=8e-4, betas=(0.9, 0.98), eps=1e-9
      )
      self.warmup_lr_scheduler = WarmupLR(self.optimizer, 40, 120, 0.96)
  
imports: import logging
  import math
  from collections import namedtuple
  from typing import List, Tuple
  import sentencepiece
  import torch
  import torchaudio
  from pytorch_lightning import LightningModule
  from torchaudio.models import Hypothesis, RNNTBeamSearch
  from torchaudio.prototype.models import conformer_rnnt_base
label_desc: Use image node_id 1 to create a new ConformerRNNTModule object from inherited base classes: LightningModule with example: obj = ConformerRNNTModule(sp_model)