def METHOD_NAME():
    x = torch.rand(2, 10, 10)
    x_lens = torch.tensor([10, 8], dtype=torch.long)
    for num_spk in range(1, 3):
        model = TCNSeparator(
            input_dim=10,
            layer=num_spk,
            stack=2,
            bottleneck_dim=3,
            hidden_dim=3,
            kernel=3,
            causal=False,
        )
        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