code: def post_process_hypos(
      hypos: List[Hypothesis], sp_model: spm.SentencePieceProcessor
  ) -> List[Tuple[str, float, List[int], List[int]]]:
      tokens_idx = 0
      score_idx = 3
      post_process_remove_list = [
          sp_model.unk_id(),
          sp_model.eos_id(),
          sp_model.pad_id(),
      ]
      filtered_hypo_tokens = [
          [
              token_index
              for token_index in h[tokens_idx][1:]
              if token_index not in post_process_remove_list
          ]
          for h in hypos
      ]
      hypos_str = [sp_model.decode(s) for s in filtered_hypo_tokens]
      hypos_ids = [h[tokens_idx][1:] for h in hypos]
      hypos_score = [[math.exp(h[score_idx])] for h in hypos]
  
      nbest_batch = list(zip(hypos_str, hypos_score, hypos_ids))
  
      return nbest_batch
  
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 for calling a global function with example usage: post_process_hypos(hypos, sp_model) and returns: nbest_batch