如何在tensorflow中使用带有tf.distribute.MirroredStrategy的keras.utils.Sequence数据生成器进行多gpu模型训练？

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.utils import Sequence

class CustomDataGenerator(Sequence):
    def __init__(self, batch_size, data):
        self.batch_size = batch_size
        self.data = data

    def __len__(self):
        return len(self.data) // self.batch_size

    def __getitem__(self, idx):
        batch_data = self.data[idx * self.batch_size:(idx + 1) * self.batch_size]
        # 在这里对batch_data进行数据预处理和增强操作
        return batch_data, labels

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    model = keras.models.Sequential()
    # 构建模型结构
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

data_generator = CustomDataGenerator(batch_size, data)

model.fit(data_generator, epochs=epochs, steps_per_epoch=len(data) // batch_size)