Tensorflow代码转到Keras

我现在有Tensortflow的代码和结构图如下，这是AC-GAN中生成器的部分，我用原生tf是可以跑通的，但当我想转到Keras中实现却很头疼。

def batch_norm(inputs, is_training=is_training, decay=0.9):
    return tf.contrib.layers.batch_norm(inputs, is_training=is_training, decay=decay)

# 构建残差块
def g_block(inputs):
    h0 = tf.nn.relu(batch_norm(conv2d(inputs, 3, 64, 1, use_bias=False)))
    h0 = batch_norm(conv2d(h0, 3, 64, 1, use_bias=False))
    h0 = tf.add(h0, inputs)
    return h0

# 生成器
# batch_size = 32
# z : shape(32, 128)
# label : shape(32, 34)
def generator(z, label):
    with tf.variable_scope('generator', reuse=None):
        d = 16
        z = tf.concat([z, label], axis=1)
        h0 = tf.layers.dense(z, units=d * d * 64)
        h0 = tf.reshape(h0, shape=[-1, d, d, 64])
        h0 = tf.nn.relu(batch_norm(h0))
        shortcut = h0

        for i in range(16):
            h0 = g_block(h0)

        h0 = tf.nn.relu(batch_norm(h0))
        h0 = tf.add(h0, shortcut)

        for i in range(3):
            h0 = conv2d(h0, 3, 256, 1, use_bias=False)
            h0 = tf.depth_to_space(h0, 2)
            h0 = tf.nn.relu(batch_norm(h0))

        h0 = tf.layers.conv2d(h0, kernel_size=9, filters=3, strides=1,
                              padding='same', activation=tf.nn.tanh, name='g', use_bias=True)
        return h0

在Keras中都是先构建Model，在Model中不断的加层

但上面的代码却是中间包含着新旧数据的计算，比如

....
shortcut = h0
....
h0 = tf.add(h0, shortcut)

难不成我还要构建另外一个model作为中间输出吗？
大佬们帮帮忙解释下，或者能不能给出翻译到Keras中应该怎么写

https://blog.csdn.net/qq_30362711/article/details/89846438