code: def test_transducer_beam_search(
      decoder_class, decoder_opts, search_opts
  ):
      token_list = ["<blank>", "a", "b", "c"]
      vocab_size = len(token_list)
  
      encoder_size = 4
  
      if decoder_class in (MEGADecoder, RWKVDecoder):
          if (
              decoder_class == RWKVDecoder
              and not torch.cuda.is_available()
          ):
              pytest.skip(
                  "A GPU is required for WKV kernel computation"
              )
  
          decoder = decoder_class(
              vocab_size, block_size=4, **decoder_opts
          )
      else:
          decoder = decoder_class(
              vocab_size, embed_size=4, **decoder_opts
          )
  
      joint_net = JointNetwork(
          vocab_size, encoder_size, 4, joint_space_size=2
      )
  
      lm = search_opts.pop(
          "lm",
          SequentialRNNLM(
              vocab_size, unit=8, nlayers=1, rnn_type="lstm"
          ),
      )
  
      beam = BeamSearchTransducer(
          decoder,
          joint_net,
          beam_size=2,
          lm=lm,
          **search_opts,
      )
  
      enc_out = torch.randn(30, encoder_size)
  
      with torch.no_grad():
          _ = beam(enc_out)
  
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 1 for calling a global function with example usage: test_transducer_beam_search(decoder_class, decoder_opts, search_opts) without return types