code: def test_SequentialRNNLM_batch_beam_search(
      rnn_type, tie_weights, dtype
  ):
      token_list = ["<blank>", "a", "b", "c", "unk", "<eos>"]
      vocab_size = len(token_list)
  
      model = SequentialRNNLM(
          vocab_size,
          nlayers=2,
          rnn_type=rnn_type,
          tie_weights=tie_weights,
      )
      beam = BatchBeamSearch(
          beam_size=3,
          vocab_size=vocab_size,
          weights={"test": 1.0},
          scorers={"test": model},
          token_list=token_list,
          sos=vocab_size - 1,
          eos=vocab_size - 1,
          pre_beam_score_key=None,
      )
      beam.to(dtype=dtype)
  
      enc = torch.randn(10, 20).type(dtype)
      with torch.no_grad():
          beam(
              x=enc,
              maxlenratio=0.0,
              minlenratio=0.0,
          )
  
file_path: espnet/test/espnet2/lm/test_seq_rnn_lm.py
imports: import pytest
  import torch
  from espnet2.lm.seq_rnn_lm import SequentialRNNLM
  from espnet.nets.batch_beam_search import BatchBeamSearch
  from espnet.nets.beam_search import BeamSearch
label_desc: Use image node_id 6 for calling a global function with example usage: test_SequentialRNNLM_batch_beam_search(rnn_type, tie_weights, dtype) without return types