def METHOD_NAME():
    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