如何在Keras网络中创建重复的结构？

from keras.layers import Dense

# 定义重复次数
num_repeats = 3

# 定义每层的单元数量
units = [32, 64, 128]

# 创建网络模型
model = Sequential()

# 使用循环创建重复的全连接层
for i in range(num_repeats):
    model.add(Dense(units[i], activation='relu'))

# 打印模型结构
model.summary()

from keras.layers import Conv2D

# 定义重复次数
num_repeats = 3

# 定义卷积核数量
filters = [32, 64, 128]

# 定义卷积核大小
kernel_size = (3, 3)

# 创建网络模型
model = Sequential()

# 定义递归函数来创建重复的卷积层
def add_conv_layer(model, num_repeats, filters, kernel_size):
    if num_repeats == 0:
        return model
    
    model.add(Conv2D(filters, kernel_size, activation='relu'))
    return add_conv_layer(model, num_repeats - 1, filters, kernel_size)

# 使用递归函数创建重复的卷积层
model = add_conv_layer(model, num_repeats, filters, kernel_size)

# 打印模型结构
model.summary()