def METHOD_NAME():
    X = np.arange(-5, 5, 0.1).reshape((-1, 1))
    X_test = np.arange(-5.05, 5.05, 0.1).reshape((-1, 1))
    y = np.sin(X)
    seed = 1
    gp = get_gp(n_dimensions=1, seed=seed, noise=1e-10, normalize_y=False)
    gp._train(X, y, optimize_hyperparameters=False)
    assert not gp.models[0]._normalize_y
    assert not hasattr(gp.models[0], "mean_y_")
    mu_hat, var_hat = gp.predict(X_test)
    gp_norm = get_gp(n_dimensions=1, seed=seed, noise=1e-10, normalize_y=True)
    gp_norm._train(X, y, optimize_hyperparameters=False)
    assert gp_norm.models[0]._normalize_y
    assert hasattr(gp_norm.models[0], "mean_y_")
    mu_hat_prime, var_hat_prime = gp_norm.predict(X_test)
    np.testing.assert_array_almost_equal(mu_hat, mu_hat_prime, decimal=4)
    np.testing.assert_array_almost_equal(var_hat, var_hat_prime, decimal=4)