是否可以在不扩展TensorFlow中计算图形的情况下手动设置模型参数值？

import tensorflow as tf

# 创建一个名为weights的可训练变量
weights = tf.Variable(tf.random_normal([2, 2]), name='weights')

# 创建一个用于设置weights的占位符
new_weights = tf.placeholder(tf.float32, shape=[2, 2])
# 使用tf.assign()手动设置weights的值
assign_weights = tf.assign(weights, new_weights)

# 创建一个会话，并运行assign_weights来设置weights的值
with tf.Session() as sess:
    # 初始化变量
    sess.run(tf.global_variables_initializer())
    
    # 设置新的权重值
    new_weights_value = [[1.0, 2.0], [3.0, 4.0]]
    sess.run(assign_weights, feed_dict={new_weights: new_weights_value})