code: def test_dpcl_e2e_separator_forward_backward_complex(
      input_dim,
      rnn_type,
      layer,
      unit,
      dropout,
      num_spk,
      predict_noise,
      emb_D,
      nonlinear,
      alpha,
      max_iteration,
  ):
      model = DPCLE2ESeparator(
          input_dim=input_dim,
          rnn_type=rnn_type,
          layer=layer,
          unit=unit,
          dropout=dropout,
          num_spk=num_spk,
          predict_noise=predict_noise,
          emb_D=emb_D,
          nonlinear=nonlinear,
          alpha=alpha,
          max_iteration=max_iteration,
      )
      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)
  
      assert isinstance(masked[0], ComplexTensor)
      assert len(masked) == num_spk
  
      masked[0].abs().mean().backward()
  
file_path: espnet/test/espnet2/enh/separator/test_dpcl_e2e_separator.py
imports: import pytest
  import torch
  from torch import Tensor
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.dpcl_e2e_separator import DPCLE2ESeparator
label_desc: Use image node_id 1 for calling a global function with example usage: test_dpcl_e2e_separator_forward_backward_complex(input_dim, rnn_type, layer, unit, dropout, num_spk, predict_noise, emb_D, nonlinear, alpha, max_iteration) without return types