code: def __init__(self, sp_model, biasing=False):
      super().__init__()
  
      self.sp_model = sp_model
      self.sp_model = spm.SentencePieceProcessor(
          model_file=self.sp_model
      )
      spm_vocab_size = self.sp_model.get_piece_size()
      self.char_list = [
          self.sp_model.id_to_piece(idx)
          for idx in range(spm_vocab_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
      self.char_list.append("<blank>")
  
      # ``conformer_rnnt_biasing_base`` hardcodes a specific Conformer RNN-T configuration.
      # For greater customizability, please refer to ``conformer_rnnt_biasing``.
      self.biasing = biasing
      self.model = conformer_rnnt_biasing_base(
          charlist=self.char_list, biasing=self.biasing
      )
      self.loss = torchaudio.transforms.RNNTLoss(
          reduction="sum", fused_log_softmax=False
      )
      self.optimizer = torch.optim.Adam(
          self.model.parameters(), lr=8e-4, betas=(0.9, 0.98), eps=1e-9
      )
      # This scheduler is for clean 100 and train 90 epochs, should change it when running longer
      self.warmup_lr_scheduler = WarmupLR(self.optimizer, 35, 60, 0.92)
      # The epoch from which the TCPGen starts to train
      self.tcpsche = self.model.tcpsche
  
      self.automatic_optimization = False
  
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.prototype.models import conformer_rnnt_biasing_base, Hypothesis, RNNTBeamSearchBiasing
label_desc: Use image node_id 1 to create a new ConformerRNNTModule object from inherited base classes: LightningModule with example: obj = ConformerRNNTModule(sp_model, biasing)