如何通过fit_generator为Keras模型提供多个输入

from keras.models import Model
from keras.layers import Input, Dense
from keras.utils import Sequence

# 创建数据生成器
class DataGenerator(Sequence):
    def __init__(self, x1, x2, y, batch_size):
        self.x1 = x1
        self.x2 = x2
        self.y = y
        self.batch_size = batch_size

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

    def __getitem__(self, idx):
        batch_x1 = self.x1[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_x2 = self.x2[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]
        return [batch_x1, batch_x2], batch_y

# 定义模型
input1 = Input(shape=(10,), name='input1')
input2 = Input(shape=(20,), name='input2')
x1 = Dense(32, activation='relu')(input1)
x2 = Dense(64, activation='relu')(input2)
concat = keras.layers.concatenate([x1, x2])
output = Dense(1, activation='sigmoid')(concat)
model = Model(inputs=[input1, input2], outputs=output)

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

# 创建数据生成器实例
batch_size = 32
train_generator = DataGenerator(x1_train, x2_train, y_train, batch_size)

# 训练模型
model.fit_generator(generator=train_generator, steps_per_epoch=len(y_train) // batch_size, epochs=10)