如何使用TensorFlow v2进行数据流编程？

import tensorflow as tf

# 创建两个常量节点
a = tf.constant(2)
b = tf.constant(3)

# 创建一个加法节点
c = tf.add(a, b)

# 创建一个会话
with tf.Session() as sess:
    # 运行数据流图
    result = sess.run(c)
    print(result)

# 创建一个变量节点
weights = tf.Variable(tf.random_normal([784, 10]))

# 定义模型
def linear_regression(x):
    return tf.matmul(x, weights)

# 定义损失函数
def loss_fn(y_true, y_pred):
    return tf.reduce_mean(tf.square(y_true - y_pred))

# 定义优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

# 训练模型
for epoch in range(num_epochs):
    with tf.GradientTape() as tape:
        # 前向传播
        y_pred = linear_regression(x_train)
        loss = loss_fn(y_train, y_pred)
    
    # 反向传播
    gradients = tape.gradient(loss, [weights])
    optimizer.apply_gradients(zip(gradients, [weights]))