如何在tf-keras中定义多输出的自定义损失函数？

import tensorflow as tf
from tensorflow import keras

class MultiOutputLoss(tf.keras.losses.Loss):
    def __init__(self, alpha=0.5, beta=0.5):
        super(MultiOutputLoss, self).__init__()
        self.alpha = alpha
        self.beta = beta

    def call(self, y_true, y_pred):
        loss1 = tf.reduce_mean(tf.square(y_true[:, 0] - y_pred[:, 0]))
        loss2 = tf.reduce_mean(tf.square(y_true[:, 1] - y_pred[:, 1]))
        total_loss = self.alpha * loss1 + self.beta * loss2
        return total_loss

model = tf.keras.models.Sequential()
# 添加模型层
# ...

model.compile(optimizer='adam', loss=MultiOutputLoss(alpha=0.5, beta=0.5))

model.fit(x_train, y_train, epochs=10, batch_size=32)