def METHOD_NAME(self, device_0, device_1):
    set_determinism(0)
    model_one = torch.nn.Sequential(_TestModelOne(10, 20, 3))
    model_two = _TestModelTwo(10, 20, 10, 4)
    model_one.to(device_0)
    model_two.to(device_1)
    # test skip layer.bias
    model_dict, ch, unch = copy_model_state(
        model_one, model_two, dst_prefix="0.", exclude_vars="layer.bias", inplace=False
    )
    model_one.load_state_dict(model_dict)
    x = np.random.randn(4, 10)
    x = torch.tensor(x, device=device_0, dtype=torch.float32)
    output = model_one(x).detach().cpu().numpy()
    expected = np.array(
        [
            [-0.360766, -0.031778, -0.770227],
            [-0.052683, -0.158559, -0.011493],
            [-0.376051, -0.224852, -0.063404],
            [0.597767, -0.679911, 0.19195],
        ]
    )
    np.testing.assert_allclose(output, expected, atol=1e-3)
    self.assertEqual(len(ch), 2)
    self.assertEqual(len(unch), 2)