如何在Keras中使用多个变量作为单输出模型的损失？

from keras import backend as K

def custom_loss(y_true, y_pred):
    loss = K.mean(K.abs(y_true - y_pred))
    return loss

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

# 假设有两个输入变量
input1 = Input(shape=(10,))
input2 = Input(shape=(20,))

# 假设有一个输出变量
output = Dense(1)(input1)

# 创建模型
model = Model(inputs=[input1, input2], outputs=output)

# 编译模型并指定自定义损失函数
model.compile(optimizer='adam', loss=custom_loss)

# 假设有100个样本
x1_train = ...
x2_train = ...
y_train = ...

# 训练模型
model.fit([x1_train, x2_train], y_train, epochs=10, batch_size=32)