如何将SequenceExample对象的读取从tf.python_io.tf_record_iterator转换为tf.data.TFRecordDataset

import tensorflow as tf

def parse_sequence_example(serialized_example):
    context_features = {
        "feature1": tf.FixedLenFeature([], dtype=tf.int64),
        "feature2": tf.FixedLenFeature([], dtype=tf.string)
    }
    sequence_features = {
        "feature3": tf.FixedLenSequenceFeature([], dtype=tf.float32),
        "feature4": tf.FixedLenSequenceFeature([], dtype=tf.string)
    }
    context_parsed, sequence_parsed = tf.parse_single_sequence_example(
        serialized=serialized_example,
        context_features=context_features,
        sequence_features=sequence_features
    )
    return context_parsed, sequence_parsed

def read_sequence_examples(filename):
    dataset = tf.data.TFRecordDataset(filename)
    dataset = dataset.map(parse_sequence_example)
    return dataset

filename = "path/to/your/tfrecord/file.tfrecord"
dataset = read_sequence_examples(filename)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()