def METHOD_NAME():
    x = tf.ones([1, 2], dtype=tf.float32)
    c0 = tf.constant(0.1 * np.asarray([[0, 1]]), dtype=tf.float32)
    h0 = tf.constant(0.1 * np.asarray([[2, 3]]), dtype=tf.float32)
    state0 = [h0, c0]
    c1 = tf.constant(0.1 * np.asarray([[4, 5]]), dtype=tf.float32)
    h1 = tf.constant(0.1 * np.asarray([[6, 7]]), dtype=tf.float32)
    state1 = [h1, c1]
    state = (state0, state1)
    const_initializer = tf.constant_initializer(0.5)
    def single_cell():
        return LayerNormLSTMCell(
            units=2,
            kernel_initializer=const_initializer,
            recurrent_initializer=const_initializer,
            bias_initializer=const_initializer,
            norm_epsilon=1e-12,
        )
    cell = keras.layers.StackedRNNCells([single_cell() for _ in range(2)])
    output_v, output_states_v = cell(x, state)
    expected_output = np.array([[-0.47406167, 0.47406143]])
    expected_state0_c = np.array([[-1.0, 1.0]])
    expected_state0_h = np.array([[-0.47406167, 0.47406143]])
    expected_state1_c = np.array([[-1.0, 1.0]])
    expected_state1_h = np.array([[-0.47406167, 0.47406143]])
    actual_state0_h = output_states_v[0][0]
    actual_state0_c = output_states_v[0][1]
    actual_state1_h = output_states_v[1][0]
    actual_state1_c = output_states_v[1][1]
    np.testing.assert_allclose(output_v, expected_output, 1e-5)
    np.testing.assert_allclose(expected_state0_c, actual_state0_c, 1e-5)
    np.testing.assert_allclose(expected_state0_h, actual_state0_h, 1e-5)
    np.testing.assert_allclose(expected_state1_c, actual_state1_c, 1e-5)
    np.testing.assert_allclose(expected_state1_h, actual_state1_h, 1e-5)
    # Test BasicLSTMCell with input_size != num_units.
    x = tf.ones([1, 3], dtype=tf.float32)
    c = tf.constant(0.1 * np.asarray([[0, 1]]), dtype=tf.float32)
    h = tf.constant(0.1 * np.asarray([[2, 3]]), dtype=tf.float32)
    state = [h, c]
    cell = LayerNormLSTMCell(
        units=2,
        kernel_initializer=const_initializer,
        recurrent_initializer=const_initializer,
        bias_initializer=const_initializer,
        norm_epsilon=1e-12,
    )
    output_v, output_states_v = cell(x, state)
    expected_h = np.array([[-0.47406167, 0.47406143]])
    expected_c = np.array([[-1.0, 1.0]])
    np.testing.assert_allclose(output_v, expected_h, 1e-5)
    np.testing.assert_allclose(output_states_v[0], expected_h, 1e-5)
    np.testing.assert_allclose(output_states_v[1], expected_c, 1e-5)