code: def test_recombine_hyps():
      decoder = StatelessDecoder(4, embed_size=4)
      joint_net = JointNetwork(4, 4, 4, joint_space_size=2)
      beam_search = BeamSearchTransducer(decoder, joint_net, 2)
  
      test_hyp = [
          Hypothesis(score=0.0, yseq=[0, 1, 2], dec_state=None),
          Hypothesis(score=12.0, yseq=[0, 1, 2], dec_state=None),
      ]
  
      final = beam_search.recombine_hyps(test_hyp)
  
      assert len(final) == 1
      assert final[0].score == np.logaddexp(0.0, 12.0)
  
file_path: espnet/test/espnet2/asr_transducer/test_beam_search_transducer.py
imports: import numpy
  import pytest
  import torch
  from espnet2.asr_transducer.beam_search_transducer import BeamSearchTransducer, Hypothesis
  from espnet2.asr_transducer.decoder.mega_decoder import MEGADecoder
  from espnet2.asr_transducer.decoder.rnn_decoder import RNNDecoder
  from espnet2.asr_transducer.decoder.rwkv_decoder import RWKVDecoder
  from espnet2.asr_transducer.decoder.stateless_decoder import StatelessDecoder
  from espnet2.asr_transducer.joint_network import JointNetwork
  from espnet2.lm.seq_rnn_lm import SequentialRNNLM
label_desc: Use image node_id 3 for calling a global function with example usage: test_recombine_hyps() without return types