code: def forward(self, batch: Batch):
      decoder = RNNTBeamSearchBiasing(
          self.model,
          self.blank_idx,
          trie=batch.tries,
          biasing=self.biasing,
      )
      hypotheses = decoder(
          batch.features.to(self.device),
          batch.feature_lengths.to(self.device),
          10,
      )
      return post_process_hypos(hypotheses, self.sp_model)[0][0]
  
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 4 for calling the ConformerRNNTModule obj's underlying member method code with example usage: obj.forward(batch) and returns: post_process_hypos