import tensorflow as tf


        results, imgs = sess.run(next_element)

        print('names: {}'.format(results['member/name']))
        print('ages: {}'.format(results['member/age']))
        print('heights: {}'.format(results['member/height']))
        print('prefer_prods: {}'.format(results['member/prefer_prods']))

        for img in imgs:
            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
            cv2.imshow('img', img)
            cv2.waitKey(-1)


def parse_function(example_proto):
    features = {'member/name': tf.io.FixedLenFeature([], tf.string),
                'member/encoded': tf.io.FixedLenFeature([], tf.string),
                'member/age': tf.io.FixedLenFeature([], tf.int64),
                'member/height': tf.io.VarLenFeature(tf.float32),
                'member/prefer_prods': tf.io.VarLenFeature(tf.int64)}
    features = tf.io.parse_single_example(example_proto, features)
    images = tf.image.decode_png(features['member/encoded'], channels=3)
    # 注意png原本有4個channel，但執行到下面的處理會出錯，所以前一行先降成3個channel。
    images = tf.image.random_brightness(images, 0.1)
    images = tf.image.random_saturation(images, 0.7, 1.3)
    images = tf.image.random_contrast(images, 0.6, 1.5)
    images = tf.image.random_flip_left_right(images)

    return features, images

