code: def test_dpcl_separator_output():
      x = torch.rand(2, 10, 10)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      for num_spk in range(1, 4):
          model = DPCLSeparator(
              input_dim=10,
              rnn_type="rnn",
              layer=2,
              unit=10,
              dropout=0.1,
              num_spk=num_spk,
              emb_D=40,
              nonlinear="relu",
          )
          model.eval()
          specs, _, others = model(x, x_lens)
          assert isinstance(specs, list)
          assert isinstance(others, dict)
          assert len(specs) == num_spk, len(specs)
          for n in range(num_spk):
              assert "tf_embedding" in others
  
imports: import pytest
  import torch
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.dpcl_separator import DPCLSeparator
label_desc: Use image node_id 4 for calling a global function with example usage: test_dpcl_separator_output() without return types