帮我写一个cycleGAN的代码

这是一个基本的 cycleGAN 的代码例子：

import tensorflow as tf

# 定义生成器和判别器
def generator(x, reuse=False):
  with tf.variable_scope('Generator', reuse=reuse):
    # 在这里定义生成器的网络结构
    return generated_output

def discriminator(x, reuse=False):
  with tf.variable_scope('Discriminator', reuse=reuse):
    # 在这里定义判别器的网络结构
    return discriminator_output

# 定义输入占位符
real_A = tf.placeholder(tf.float32, shape=[None, 256, 256, 3])
real_B = tf.placeholder(tf.float32, shape=[None, 256, 256, 3])

# 使用生成器和判别器
fake_B = generator(real_A)
fake_A = generator(real_B, reuse=True)

rec_A = generator(fake_B, reuse=True)
rec_B = generator(fake_A, reuse=True)

real_A_dis = discriminator(real_A)
real_B_dis = discriminator(real_B)
fake_A_dis = discriminator(fake_A, reuse=True)
fake_B_dis = discriminator(fake_B, reuse=True)

# 定义损失函数
adversarial_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_B_dis, labels=tf.ones_like(fake_B_dis))) + tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_A_dis, labels=tf.ones_like(fake_A_dis))) + tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real_A_dis, labels=tf.zeros_like(real_A_dis))) + tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real_B_dis, labels=tf.zeros_like(real_B_dis)))
cycle_loss = tf.reduce_mean(tf.abs(real_A - rec_A)) + tf.reduce_mean(tf.abs(real_B - rec_B))

# 定义优化器
optimizer = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)

# 定义训练过程
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VAR