def METHOD_NAME():
    for k in [5, 10, 100]:
        for alpha in [0.3, 0.7]:
            optimizer = tf.keras.optimizers.get("adam")
            vals, quick_vars = run_dense_sample(k, optimizer)
            optimizer = Lookahead("adam", sync_period=k, slow_step_size=alpha)
            _, slow_vars = run_dense_sample(k, optimizer)
            for val, quick, slow in zip(vals, quick_vars, slow_vars):
                expected = val + (quick - val) * alpha
                np.testing.assert_allclose(
                    expected.numpy(), slow.numpy(), rtol=1e-06, atol=1e-06
                )