如何在tensorflow keras中访问自定义层的递归层

import tensorflow as tf
from tensorflow.keras import layers

class CustomLayer(layers.Layer):
    def __init__(self):
        super(CustomLayer, self).__init__()
        self.recursive_layer = layers.LSTM(64)  # 定义递归层

    def call(self, inputs):
        x = self.recursive_layer(inputs)  # 前向传播逻辑
        return x

# 创建模型
model = tf.keras.Sequential()
model.add(layers.Dense(32, activation='relu'))
model.add(CustomLayer())  # 调用自定义层
model.add(layers.Dense(10, activation='softmax'))

# 编译和训练模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=32)