def METHOD_NAME():
    x = torch.rand(1, 10, 10)
    x_lens = torch.tensor([10], dtype=torch.long)
    for num_spk in range(1, 4):
        model = DPCLE2ESeparator(
            input_dim=10,
            rnn_type="rnn",
            layer=2,
            unit=10,
            dropout=0.1,
            num_spk=num_spk,
            emb_D=40,
            nonlinear="relu",
            alpha=5.0,
            max_iteration=100,
        )
        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