如何通过tensorflow-dataset api使用tensorflow-hub模块

import tensorflow as tf
import tensorflow_hub as hub

module_url = "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4"
module = hub.KerasLayer(module_url)

dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels))

def preprocess_image(image, label):
    image = tf.image.decode_jpeg(tf.io.read_file(image), channels=3)
    image = tf.image.resize(image, (224, 224))
    image = tf.keras.applications.mobilenet_v2.preprocess_input(image)
    return image, label

dataset = dataset.map(preprocess_image)

train_dataset = dataset.take(train_size)
val_dataset = dataset.skip(train_size)

train_dataset = train_dataset.shuffle(buffer_size=1000).batch(batch_size)
val_dataset = val_dataset.batch(batch_size)

model = tf.keras.Sequential([
    module,
    tf.keras.layers.Dense(num_classes, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(train_dataset, epochs=num_epochs, validation_data=val_dataset)