code: def recombine_hyps(hyps: List[Hypothesis]) -> List[Hypothesis]:
      """Recombine hypotheses with same label ID sequence.
  
      Args:
          hyps: Hypotheses.
  
      Returns:
         final: Recombined hypotheses.
  
      """
      final = []
  
      for hyp in hyps:
          seq_final = [f.yseq for f in final if f.yseq]
  
          if hyp.yseq in seq_final:
              seq_pos = seq_final.index(hyp.yseq)
  
              final[seq_pos].score = np.logaddexp(
                  final[seq_pos].score, hyp.score
              )
          else:
              final.append(hyp)
  
      return final
  
file_path: espnet/espnet/nets/pytorch_backend/transducer/utils.py
imports: import os
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import torch
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.nets.transducer_decoder_interface import ExtendedHypothesis, Hypothesis
label_desc: Use image node_id 9 for calling a global function with example usage: recombine_hyps(hyps) and returns: final