code: def test_dccrn_separator_forward_backward_complex(
      input_dim,
      num_spk,
      rnn_layer,
      rnn_units,
      masking_mode,
      use_clstm,
      bidirectional,
      use_cbn,
      kernel_size,
      use_builtin_complex,
      use_noise_mask,
  ):
      model = DCCRNSeparator(
          input_dim=input_dim,
          num_spk=num_spk,
          rnn_layer=rnn_layer,
          rnn_units=rnn_units,
          masking_mode=masking_mode,
          use_clstm=use_clstm,
          bidirectional=bidirectional,
          use_cbn=use_cbn,
          kernel_size=kernel_size,
          kernel_num=[4, 8, 16],
          use_builtin_complex=use_builtin_complex,
          use_noise_mask=use_noise_mask,
      )
      model.train()
  
      real = torch.rand(2, 10, input_dim)
      imag = torch.rand(2, 10, input_dim)
      x = ComplexTensor(real, imag)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      masked, flens, others = model(x, ilens=x_lens)
  
      if use_builtin_complex and is_torch_1_9_plus:
          assert isinstance(masked[0], torch.Tensor)
      else:
          assert isinstance(masked[0], ComplexTensor)
      assert len(masked) == num_spk
  
      masked[0].abs().mean().backward()
  
imports: import pytest
  import torch
  from packaging.version import parse
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.dccrn_separator import DCCRNSeparator
label_desc: Use image node_id 1 for calling a global function with example usage: test_dccrn_separator_forward_backward_complex(input_dim, num_spk, rnn_layer, rnn_units, masking_mode, use_clstm, bidirectional, use_cbn, kernel_size, use_builtin_complex, use_noise_mask) without return types