如何用TensorFlow实现动态网络结构？

import tensorflow as tf

input_data = tf.placeholder(tf.float32, shape=[None, input_size])

hidden_units = 100
hidden_layer = tf.layers.dense(input_data, hidden_units, activation=tf.nn.relu)
output_layer = tf.layers.dense(hidden_layer, num_classes)

new_hidden_units = 200
new_hidden_layer = tf.layers.dense(input_data, new_hidden_units, activation=tf.nn.relu)
new_output_layer = tf.layers.dense(new_hidden_layer, num_classes)

labels = tf.placeholder(tf.int32, shape=[None])
loss = tf.losses.sparse_softmax_cross_entropy(labels, output_layer)
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(num_epochs):
        sess.run(train_op, feed_dict={input_data: train_data, labels: train_labels})