如何使用tf.data创建图像序列样本？

import tensorflow as tf
import glob

image_files = glob.glob("path_to_image_directory/*.jpg")

dataset = tf.data.Dataset.from_tensor_slices(image_files)

def preprocess_image(image_path):
    # 加载图像
    image = tf.io.read_file(image_path)
    # 解码图像
    image = tf.image.decode_jpeg(image, channels=3)
    # 对图像进行预处理，例如调整大小、归一化等
    image = preprocess_function(image)
    return image

dataset = dataset.map(preprocess_image)

sequence_length = 5
sequence_stride = 1

def create_image_sequence(image):
    # 获取图像序列
    sequence = tf.data.Dataset.from_tensor_slices(image)
    # 将图像序列按窗口大小和步长进行切割
    sequence = sequence.window(sequence_length, sequence_stride, drop_remainder=True)
    # 将切割后的图像序列转换为列表
    sequence = sequence.flat_map(lambda x: x.batch(sequence_length))
    return sequence

dataset = dataset.flat_map(create_image_sequence)

dataset = dataset.shuffle(buffer_size=1000)

batch_size = 32
prefetch_size = tf.data.experimental.AUTOTUNE

dataset = dataset.batch(batch_size).prefetch(prefetch_size)