code: def test_dpcl_separator_forward_backward_real(
      input_dim,
      rnn_type,
      layer,
      unit,
      dropout,
      num_spk,
      emb_D,
      nonlinear,
  ):
      model = DPCLSeparator(
          input_dim=input_dim,
          rnn_type=rnn_type,
          layer=layer,
          unit=unit,
          dropout=dropout,
          num_spk=num_spk,
          emb_D=emb_D,
          nonlinear=nonlinear,
      )
      model.train()
  
      x = torch.rand(2, 10, input_dim)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      masked, flens, others = model(x, ilens=x_lens)
  
      assert "tf_embedding" in others
  
      others["tf_embedding"].abs().mean().backward()
  
imports: import pytest
  import torch
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.dpcl_separator import DPCLSeparator
label_desc: Use image node_id 2 for calling a global function with example usage: test_dpcl_separator_forward_backward_real(input_dim, rnn_type, layer, unit, dropout, num_spk, emb_D, nonlinear) without return types