如何为Keras模型编写自定义dataGenetator

from keras.utils import Sequence
import numpy as np

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

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

    def __getitem__(self, index):
        batch_data = self.data[index * self.batch_size:(index + 1) * self.batch_size]
        batch_labels = self.labels[index * self.batch_size:(index + 1) * self.batch_size]
        return self.preprocess(batch_data, batch_labels)

    def preprocess(self, batch_data, batch_labels):
        # 在这里进行数据预处理，如图像的读取、缩放、归一化等操作
        # 返回预处理后的数据和标签
        return batch_data, batch_labels

train_data = ...
train_labels = ...
batch_size = ...

train_generator = CustomDataGenerator(train_data, train_labels, batch_size)

model.fit_generator(train_generator, ...)