code: def test_TransformerDecoder_backward(
      input_layer, normalize_before, use_output_layer, decoder_class
  ):
      decoder = decoder_class(
          10,
          12,
          input_layer=input_layer,
          normalize_before=normalize_before,
          use_output_layer=use_output_layer,
          linear_units=10,
      )
      x = torch.randn(2, 9, 12)
      x_lens = torch.tensor([9, 7], dtype=torch.long)
      if input_layer == "embed":
          t = torch.randint(0, 10, [2, 4], dtype=torch.long)
      else:
          t = torch.randn(2, 4, 10)
      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()
  
file_path: espnet/test/espnet2/asr/decoder/test_transformer_decoder.py
imports: import pytest
  import torch
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.transformer_decoder import DynamicConvolution2DTransformerDecoder, DynamicConvolutionTransformerDecoder, LightweightConvolution2DTransformerDecoder, LightweightConvolutionTransformerDecoder, TransformerDecoder
  from espnet.nets.batch_beam_search import BatchBeamSearch
  from espnet.nets.batch_beam_search_online_sim import BatchBeamSearchOnlineSim
  from espnet.nets.beam_search import BeamSearch
  from espnet.nets.scorers.ctc import CTCPrefixScorer
label_desc: Use image node_id 1 for calling a global function with example usage: test_TransformerDecoder_backward(input_layer, normalize_before, use_output_layer, decoder_class) without return types