将具有张量特性的tf.train.Dataset序列化为tfrecord文件？

import tensorflow as tf

def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _float_feature(value):
    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))

def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

def serialize_example(feature1, feature2, label):
    feature = {
        'feature1': _float_feature(feature1),
        'feature2': _bytes_feature(feature2),
        'label': _int64_feature(label)
    }
    example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
    return example_proto.SerializeToString()

dataset = tf.data.Dataset.from_tensor_slices((features1, features2, labels))

serialized_dataset = dataset.map(serialize_example)

writer = tf.data.experimental.TFRecordWriter('dataset.tfrecord')

writer.write(serialized_dataset)