code: def forward(self, batch):
      decoder = RNNTBeamSearch(self.model, self.blank_idx)
      x = self.frontend(batch.inputs.to(self.device))
      hypotheses = decoder(
          x, batch.input_lengths.to(self.device), beam_width=20
      )
      return post_process_hypos(hypotheses, self.sp_model)[0][0]
  
imports: import itertools
  import math
  from collections import namedtuple
  from typing import List, Tuple
  import sentencepiece
  import torch
  import torchaudio
  from models.conformer_rnnt import conformer_rnnt
  from models.emformer_rnnt import emformer_rnnt
  from models.resnet import video_resnet
  from models.resnet1d import audio_resnet
  from pytorch_lightning import LightningModule
  from schedulers import WarmupCosineScheduler
  from torchaudio.models import Hypothesis, RNNTBeamSearch
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