code: def test_S4Decoder_backward(
      input_layer, prenorm, n_layers, norm, residual, drop_path
  ):
      # Skip test for the lower pytorch versions
      if not is_torch_1_10_plus:
          return
      layer = [
          {
              "_name_": "s4",
              "keops": True,
          },  # Do not use custom Cauchy kernel (CUDA)
          {"_name_": "mha", "n_head": 4},
          {"_name_": "ff"},
      ]
      decoder = S4Decoder(
          vocab_size=10,
          encoder_output_size=12,
          input_layer=input_layer,
          prenorm=prenorm,
          n_layers=n_layers,
          layer=layer,
          norm=norm,
          residual=residual,
          drop_path=drop_path,
      )
      x = torch.randn(2, 9, 12)
      x_lens = torch.tensor([9, 7], dtype=torch.long)
      t = torch.randint(0, 10, [2, 4], dtype=torch.long)
      t_lens = torch.tensor([4, 3], dtype=torch.long)
      z_all, ys_in_lens = decoder(x, x_lens, t, t_lens)
      z_all.sum().backward()
  
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 1 for calling a global function with example usage: test_S4Decoder_backward(input_layer, prenorm, n_layers, norm, residual, drop_path) without return types