如何在keras的DataGenerator中添加多堆预处理函数？

from keras.utils import Sequence
from keras.preprocessing.image import ImageDataGenerator

class CustomDataGenerator(Sequence):
    def __init__(self, data, labels, batch_size):
        self.data = data
        self.labels = labels
        self.batch_size = batch_size
        self.datagen = ImageDataGenerator(rotation_range=20, width_shift_range=0.2, height_shift_range=0.2)

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

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

        # 加载原始数据并进行预处理
        processed_data = self.load_and_preprocess(batch_data)

        # 添加多个预处理函数
        processed_data = self.data_augmentation(processed_data)

        return processed_data, batch_labels

    def load_and_preprocess(self, data):
        # 加载原始数据并进行预处理操作
        # ...

        return processed_data

    def data_augmentation(self, data):
        # 使用ImageDataGenerator类实现数据增强操作
        augmented_data = self.datagen.flow(data, shuffle=False).next()

        return augmented_data