code: def test_S4Decoder_batch_beam_search(
      input_layer, prenorm, n_layers, norm, residual, drop_path, dtype
  ):
      # Skip test for the lower pytorch versions
      if not is_torch_1_10_plus:
          return
      token_list = ["<blank>", "a", "b", "c", "unk", "<eos>"]
      vocab_size = len(token_list)
      encoder_output_size = 4
      layer = [
          {
              "_name_": "s4",
              "keops": True,
          },  # Do not use custom Cauchy kernel
          {"_name_": "mha", "n_head": 4},
          {"_name_": "ff"},
      ]
  
      decoder = S4Decoder(
          vocab_size=vocab_size,
          encoder_output_size=encoder_output_size,
          input_layer=input_layer,
          prenorm=prenorm,
          n_layers=n_layers,
          layer=layer,
          norm=norm,
          residual=residual,
          drop_path=drop_path,
      )
      beam = BatchBeamSearch(
          beam_size=3,
          vocab_size=vocab_size,
          weights={"test": 1.0},
          scorers={"test": decoder},
          token_list=token_list,
          sos=vocab_size - 1,
          eos=vocab_size - 1,
          pre_beam_score_key=None,
      )
      beam.to(dtype=dtype).eval()
  
      for module in beam.nn_dict.test.modules():
          if hasattr(module, "setup_step"):
              module.setup_step()
  
      enc = torch.randn(10, encoder_output_size).type(dtype)
      with torch.no_grad():
          beam(
              x=enc,
              maxlenratio=0.0,
              minlenratio=0.0,
          )
  
imports: import pytest
  import torch
  from packaging.version import parse
  from espnet2.asr.decoder.s4_decoder import S4Decoder
  from espnet.nets.batch_beam_search import BatchBeamSearch
label_desc: Use image node_id 2 for calling a global function with example usage: test_S4Decoder_batch_beam_search(input_layer, prenorm, n_layers, norm, residual, drop_path, dtype) without return types