code: def test_MLMDecoder_backward(
      input_layer, normalize_before, use_output_layer
  ):
      vocab_size = 10
      decoder = MLMDecoder(
          vocab_size,
          12,
          linear_units=10,
          num_blocks=2,
          input_layer=input_layer,
          normalize_before=normalize_before,
          use_output_layer=use_output_layer,
      )
      x = torch.randn(2, 9, 12)
      x_lens = torch.tensor([9, 7], dtype=torch.long)
      if input_layer == "embed":
          t = torch.randint(0, vocab_size + 1, [2, 4], dtype=torch.long)
      else:
          t = torch.randn(2, 4, vocab_size + 1)
      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 espnet2.asr.decoder.mlm_decoder import MLMDecoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_MLMDecoder_backward(input_layer, normalize_before, use_output_layer) without return types