在tensforflow2中重新初始化某些层的权重的正确方法是什么？

import tensorflow as tf

# 创建一个模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# 打印模型的初始权重
print("初始权重：")
for layer in model.layers:
    print(layer.get_weights())

# 重新初始化某些层的权重
layers_to_reset = [0]  # 需要重新初始化权重的层的索引
for layer_index in layers_to_reset:
    layer = model.layers[layer_index]
    num_units = layer.units  # 获取层的单元数
    initializer = tf.keras.initializers.GlorotUniform()  # 使用GlorotUniform初始化器重新初始化权重
    new_weights = [initializer(shape=(layer.input_shape[-1], num_units)),  # 重新初始化权重
                   tf.zeros(shape=(num_units,))]  # 重新初始化偏置
    layer.set_weights(new_weights)  # 设置新的权重

# 打印重新初始化后的权重
print("重新初始化后的权重：")
for layer in model.layers:
    print(layer.get_weights())