code: def forward(self, batch: Batch):
      decoder = RNNTBeamSearch(self.model, self.blank_idx)
      hypotheses = decoder(
          batch.features.to(self.device),
          batch.feature_lengths.to(self.device),
          20,
      )
      return post_process_hypos(hypotheses, self.sp_model)[0][0]
  
imports: import os
  from functools import partial
  from typing import List
  import sentencepiece
  import torch
  import torchaudio
  from common import Batch, batch_by_token_count, FunctionalModule, GlobalStatsNormalization, piecewise_linear_log, post_process_hypos, spectrogram_transform, WarmupLR
  from pytorch_lightning import LightningModule
  from torchaudio.models import emformer_rnnt_base, RNNTBeamSearch
label_desc: Use image node_id 10 for calling the TEDLIUM3RNNTModule obj's underlying member method code with example usage: obj.forward(batch) and returns: post_process_hypos