def METHOD_NAME(self, device):
    """ test compute rounding loss """
    tf.compat.v1.reset_default_graph()
    np.random.seed(0)
    alpha = np.random.rand(1, 3, 12, 12)
    reg_param = 0.01
    with tf.device(device):
        beta_tensor = tf.compat.v1.placeholder(dtype=tf.float32, shape=[])
        warm_start_tensor = tf.compat.v1.placeholder(dtype=tf.bool, shape=[])
        alpha_tensor = tf.convert_to_tensor(alpha, dtype=tf.float32)
        round_loss_tensor = AdaroundLoss.compute_round_loss(alpha_tensor, reg_param, warm_start_tensor, beta_tensor)
    session = tf.compat.v1.Session()
    beta = 17.388903112897612
    warm_start = True
    rounding_loss_1 = session.run(round_loss_tensor, feed_dict={beta_tensor: beta, warm_start_tensor: warm_start})
    # Since warm start is 0.2 (20%), cut iter < 2000 (20% of 10000 iterations) will have rounding loss = 0
    self.assertEqual(rounding_loss_1, 0)
    beta = 4.636038969321072
    warm_start = False
    rounding_loss_2 = session.run(round_loss_tensor, feed_dict={beta_tensor: beta, warm_start_tensor: warm_start})
    self.assertAlmostEqual(rounding_loss_2, 4.266156963161077, places=4)
    session.close()