如何在Keras模型子类API中使用多个模型

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

class SubModel(keras.Model):
    def __init__(self):
        super(SubModel, self).__init__()
        self.dense = layers.Dense(64, activation='relu')
        self.dropout = layers.Dropout(0.5)
        self.output_layer = layers.Dense(10, activation='softmax')

    def call(self, inputs):
        x = self.dense(inputs)
        x = self.dropout(x)
        return self.output_layer(x)

class MainModel(keras.Model):
    def __init__(self):
        super(MainModel, self).__init__()
        self.sub_model1 = SubModel()
        self.sub_model2 = SubModel()

    def call(self, inputs):
        x1 = self.sub_model1(inputs)
        x2 = self.sub_model2(inputs)
        return x1, x2

# 创建模型实例
model = MainModel()

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

# 训练模型
model.fit(x_train, y_train, batch_size=64, epochs=10, validation_data=(x_val, y_val))