如何在多个图像输入和一个输出的情况下使用fit_generator？

from keras.models import Model
from keras.layers import Input, Dense
from keras.preprocessing.image import ImageDataGenerator

# 创建数据生成器
datagen = ImageDataGenerator(rescale=1./255)

# 定义模型
input1 = Input(shape=(32, 32, 3))
input2 = Input(shape=(32, 32, 3))
x = Dense(64, activation='relu')(input1)
y = Dense(64, activation='relu')(input2)
output = Dense(10, activation='softmax')(x)
model = Model(inputs=[input1, input2], outputs=output)

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 调用fit_generator函数
train_generator = datagen.flow_from_directory('train_data', target_size=(32, 32), batch_size=32, class_mode='categorical')
validation_generator = datagen.flow_from_directory('validation_data', target_size=(32, 32), batch_size=32, class_mode='categorical')
model.fit_generator(train_generator, steps_per_epoch=2000, epochs=50, validation_data=validation_generator, validation_steps=800)