def METHOD_NAME(Ntrain: int, Ntest: int, D: int) -> None:
    data = rng.randn(Ntrain, D), rng.randn(Ntrain, 1)
    Xtest, Ytest = rng.randn(Ntest, D), rng.randn(Ntest, 1)
    kernel = Matern32() + gpflow.kernels.White()
    model_gp = gpflow.models.GPR(data, kernel=kernel)
    mu_y, var_y = model_gp.predict_y(Xtest)
    data = Xtest, Ytest
    log_density = model_gp.predict_log_density(data)
    log_density_hand = np.squeeze(
        -0.5 * np.log(2 * np.pi) - 0.5 * np.log(var_y) - 0.5 * np.square(mu_y - Ytest) / var_y,
        axis=-1,
    )
    assert np.allclose(log_density_hand, log_density)