code: def test_dc_crn_separator_output():
      real = torch.rand(2, 10, 17)
      imag = torch.rand(2, 10, 17)
      x = (
          torch.complex(real, imag)
          if is_torch_1_9_plus
          else ComplexTensor(real, imag)
      )
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      for num_spk in range(1, 3):
          model = DC_CRNSeparator(
              input_dim=17,
              num_spk=num_spk,
              input_channels=[2, 2, 4],
          )
          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_dc_crn_separator.py
imports: import pytest
  import torch
  from packaging.version import parse
  from torch_complex import ComplexTensor
  from espnet2.enh.layers.complex_utils import is_complex
  from espnet2.enh.separator.dc_crn_separator import DC_CRNSeparator
label_desc: Use image node_id 5 for calling a global function with example usage: test_dc_crn_separator_output() without return types