code: def test_dan_separator_output():
      x = torch.rand(1, 10, 10)
      x_lens = torch.tensor([10], dtype=torch.long)
  
      for num_spk in range(1, 4):
          model = DANSeparator(
              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)
          for n in range(num_spk):
              assert "mask_spk{}".format(n + 1) in others
              assert (
                  specs[n].shape
                  == others["mask_spk{}".format(n + 1)].shape
              )
  
file_path: espnet/test/espnet2/enh/separator/test_dan_separator.py
imports: import pytest
  import torch
  from torch import Tensor
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.dan_separator import DANSeparator
label_desc: Use image node_id 4 for calling a global function with example usage: test_dan_separator_output() without return types