如何在keras中添加自定义图像锐化层？

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

class SharpeningLayer(Layer):
    def __init__(self, **kwargs):
        super(SharpeningLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        super(SharpeningLayer, self).build(input_shape)

    def call(self, inputs):
        kernel = tf.constant([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=tf.float32)
        kernel = tf.reshape(kernel, [3, 3, 1, 1])
        sharpened_image = tf.nn.conv2d(inputs, kernel, strides=[1, 1, 1, 1], padding='SAME')
        return sharpened_image + inputs

    def compute_output_shape(self, input_shape):
        return input_shape

# 使用自定义锐化层
model = tf.keras.Sequential()
model.add(SharpeningLayer(input_shape=(32, 32, 3)))
model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation='softmax'))

# 编译和训练模型
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))