code: def test_rnn_separator_output():
      real = torch.rand(2, 10, 9)
      imag = torch.rand(2, 10, 9)
      x = ComplexTensor(real, imag)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      for num_spk in range(1, 3):
          model = DCCRNSeparator(
              input_dim=9,
              num_spk=num_spk,
              rnn_units=32,
              kernel_num=[4, 8, 16],
          )
          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
              )
  
imports: import pytest
  import torch
  from packaging.version import parse
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.dccrn_separator import DCCRNSeparator
label_desc: Use image node_id 3 for calling a global function with example usage: test_rnn_separator_output() without return types