如何在“编译”时在急切模式下执行TensorFlow 2 Keras顺序模型？

import tensorflow as tf

# 设置急切模式
tf.config.run_functions_eagerly(True)

# 构建顺序模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

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

# 加载数据并训练模型
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape((60000, 784))
x_test = x_test.reshape((10000, 784))
x_train = x_train / 255.0
x_test = x_test / 255.0

model.fit(x_train, y_train, epochs=5, batch_size=32)

# 在测试集上评估模型
test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test accuracy:', test_acc)