How to use the nni.choice function in nni

import nni
def max_pool(k):
    pass

h_conv1 = 1
nni.choice({'foo': foo, 'bar': bar})(1)
conv_size = nni.choice({2: 2, 3: 3, 5: 5, 7: 7}, name='conv_size')
abc = nni.choice({'2': '2', 3: 3, '(5 * 6)': 5 * 6, 7: 7}, name='abc')
h_pool1 = nni.function_choice({'max_pool': lambda : max_pool(h_conv1),
    'h_conv1': lambda : h_conv1,
    'avg_pool': lambda : avg_pool(h_conv2, h_conv3)}
)
h_pool1 = nni.function_choice({'max_pool(h_conv1)': lambda : max_pool(
    h_conv1), 'avg_pool(h_conv2, h_conv3)': lambda : avg_pool(h_conv2,
    h_conv3)}, name='max_pool')
h_pool2 = nni.function_choice({'max_poo(h_conv1)': lambda : max_poo(h_conv1
    ), '(2 * 3 + 4)': lambda : 2 * 3 + 4, '(lambda x: 1 + x)': lambda : lambda
    x: 1 + x}, name='max_poo')
tmp = nni.qlognormal(1.2, 3, 4.5)
test_acc = 1
nni.report_intermediate_result(test_acc)
test_acc = 2

import nni


def max_pool(k):
    pass


h_conv1 = 1
conv_size = nni.choice({2: 2, 3: 3, 5: 5, 7: 7}, name='conv_size')
abc = nni.choice({'2': '2', 3: 3, '(5 * 6)': 5 * 6, "{(1): 2, '3': 4}": {(1
    ): 2, '3': 4}, '[1, 2, 3]': [1, 2, 3]}, name='abc')
h_pool1 = nni.function_choice({'max_pool(h_conv1)': lambda : max_pool(
    h_conv1), 'avg_pool(h_conv2, h_conv3)': lambda : avg_pool(h_conv2,
    h_conv3)}, name='max_pool')
h_pool2 = nni.function_choice({'max_poo(h_conv1)': lambda : max_poo(h_conv1
    ), '(2 * 3 + 4)': lambda : 2 * 3 + 4, '(lambda x: 1 + x)': lambda : lambda
    x: 1 + x}, name='max_poo')
test_acc = 1
nni.report_intermediate_result(test_acc)
test_acc = 2
nni.report_final_result(test_acc)

mnist_network = MnistNetwork()
    mnist_network.build_network()
    logger.debug('Mnist build network done.')
    graph_location = tempfile.mkdtemp()
    logger.debug('Saving graph to: %s' % graph_location)
    train_writer = tf.summary.FileWriter(graph_location)
    train_writer.add_graph(tf.get_default_graph())
    test_acc = 0.0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        batch_num = 200
        for i in range(batch_num):
            batch_size = nni.choice({50: 50, 250: 250, 500: 500}, name=
                'batch_size')
            batch = mnist.train.next_batch(batch_size)
            dropout_rate = nni.choice({1: 1, 5: 5}, name='dropout_rate')
            mnist_network.train_step.run(feed_dict={mnist_network.x: batch[
                0], mnist_network.y: batch[1], mnist_network.keep_prob:
                dropout_rate})
            if i % 100 == 0:
                test_acc = mnist_network.accuracy.eval(feed_dict={
                    mnist_network.x: mnist.test.images, mnist_network.y:
                    mnist.test.labels, mnist_network.keep_prob: 1.0})
                nni.report_intermediate_result(test_acc)
        test_acc = mnist_network.accuracy.eval(feed_dict={mnist_network.x:
            mnist.test.images, mnist_network.y: mnist.test.labels,
            mnist_network.keep_prob: 1.0})
        nni.report_final_result(test_acc)

import nni


def max_pool(k):
    pass


h_conv1 = 1
conv_size = nni.choice({2: 2, 3: 3, 5: 5, 7: 7}, name='conv_size')
abc = nni.choice({'2': '2', 3: 3, '(5 * 6)': 5 * 6, "{(1): 2, '3': 4}": {(1
    ): 2, '3': 4}, '[1, 2, 3]': [1, 2, 3]}, name='abc')
h_pool1 = nni.function_choice({'max_pool(h_conv1)': lambda : max_pool(
    h_conv1), 'avg_pool(h_conv2, h_conv3)': lambda : avg_pool(h_conv2,
    h_conv3)}, name='max_pool')
h_pool2 = nni.function_choice({'max_poo(h_conv1)': lambda : max_poo(h_conv1
    ), '(2 * 3 + 4)': lambda : 2 * 3 + 4, '(lambda x: 1 + x)': lambda : lambda
    x: 1 + x}, name='max_poo')
test_acc = 1
nni.report_intermediate_result(test_acc)
test_acc = 2
nni.report_final_result(test_acc)

channel_2_num=params['channel_2_num'], conv_size=params['conv_size'
        ], hidden_size=params['hidden_size'], pool_size=params['pool_size'],
        learning_rate=params['learning_rate'])
    mnist_network.build_network()
    logger.debug('Mnist build network done.')
    graph_location = tempfile.mkdtemp()
    logger.debug('Saving graph to: %s', graph_location)
    train_writer = tf.summary.FileWriter(graph_location)
    train_writer.add_graph(tf.get_default_graph())
    test_acc = 0.0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        batch_num = nni.choice(50, 250, 500, name='batch_num')
        for i in range(batch_num):
            batch = mnist.train.next_batch(batch_num)
            dropout_rate = nni.choice(1, 5, name='dropout_rate')
            mnist_network.train_step.run(feed_dict={mnist_network.images:
                batch[0], mnist_network.labels: batch[1], mnist_network.
                keep_prob: dropout_rate})
            if i % 100 == 0:
                test_acc = mnist_network.accuracy.eval(feed_dict={
                    mnist_network.images: mnist.test.images, mnist_network.
                    labels: mnist.test.labels, mnist_network.keep_prob: 1.0})
                nni.report_intermediate_result(test_acc)
                logger.debug('test accuracy %g', test_acc)
                logger.debug('Pipe send intermediate result done.')
        test_acc = mnist_network.accuracy.eval(feed_dict={mnist_network.
            images: mnist.test.images, mnist_network.labels: mnist.test.
            labels, mnist_network.keep_prob: 1.0})
        nni.report_final_result(test_acc)
        logger.debug('Final result is %g', test_acc)
        logger.debug('Send final result done.')

mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'],
                                 channel_2_num=params['channel_2_num'],
                                 pool_size=params['pool_size'])
    mnist_network.build_network()
    logger.debug('Mnist build network done.')

    # Write log
    graph_location = tempfile.mkdtemp()
    logger.debug('Saving graph to: %s', graph_location)
    train_writer = tf.summary.FileWriter(graph_location)
    train_writer.add_graph(tf.get_default_graph())

    test_acc = 0.0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        batch_num = nni.choice(50, 250, 500, name='batch_num')
        for i in range(batch_num):
            batch = mnist.train.next_batch(batch_num)
            dropout_rate = nni.choice(1, 5, name='dropout_rate')
            mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
                                                    mnist_network.labels: batch[1],
                                                    mnist_network.keep_prob: dropout_rate}
                                        )

            if i % 100 == 0:
                test_acc = mnist_network.accuracy.eval(
                    feed_dict={mnist_network.images: mnist.test.images,
                               mnist_network.labels: mnist.test.labels,
                               mnist_network.keep_prob: 1.0})

                nni.report_intermediate_result(test_acc)
                logger.debug('test accuracy %g', test_acc)

mnist_network.build_network()
    logger.debug('Mnist build network done.')

    # Write log
    graph_location = tempfile.mkdtemp()
    logger.debug('Saving graph to: %s', graph_location)
    train_writer = tf.summary.FileWriter(graph_location)
    train_writer.add_graph(tf.get_default_graph())

    test_acc = 0.0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        batch_num = nni.choice(50, 250, 500, name='batch_num')
        for i in range(batch_num):
            batch = mnist.train.next_batch(batch_num)
            dropout_rate = nni.choice(1, 5, name='dropout_rate')
            mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
                                                    mnist_network.labels: batch[1],
                                                    mnist_network.keep_prob: dropout_rate}
                                        )

            if i % 100 == 0:
                test_acc = mnist_network.accuracy.eval(
                    feed_dict={mnist_network.images: mnist.test.images,
                               mnist_network.labels: mnist.test.labels,
                               mnist_network.keep_prob: 1.0})

                nni.report_intermediate_result(test_acc)
                logger.debug('test accuracy %g', test_acc)
                logger.debug('Pipe send intermediate result done.')

        test_acc = mnist_network.accuracy.eval(

def __init__(self, channel_1_num, channel_2_num, conv_size, hidden_size,
        pool_size, learning_rate, x_dim=784, y_dim=10):
        self.channel_1_num = channel_1_num
        self.channel_2_num = channel_2_num
        self.conv_size = nni.choice(2, 3, 5, 7, name='self.conv_size')
        self.hidden_size = nni.choice(124, 512, 1024, name='self.hidden_size')
        self.pool_size = pool_size
        self.learning_rate = nni.uniform(0.0001, 0.1, name='self.learning_rate'
            )
        self.x_dim = x_dim
        self.y_dim = y_dim
        self.images = tf.placeholder(tf.float32, [None, self.x_dim], name=
            'input_x')
        self.labels = tf.placeholder(tf.float32, [None, self.y_dim], name=
            'input_y')
        self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
        self.train_step = None
        self.accuracy = None

print('Mnist download data done.')
    logger.debug('Mnist download data done.')
    mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'],
        channel_2_num=params['channel_2_num'], conv_size=params['conv_size'
        ], hidden_size=params['hidden_size'], pool_size=params['pool_size'],
        learning_rate=params['learning_rate'])
    mnist_network.build_network()
    logger.debug('Mnist build network done.')
    graph_location = tempfile.mkdtemp()
    logger.debug('Saving graph to: %s', graph_location)
    train_writer = tf.summary.FileWriter(graph_location)
    train_writer.add_graph(tf.get_default_graph())
    test_acc = 0.0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        batch_num = nni.choice(50, 250, 500, name='batch_num')
        for i in range(batch_num):
            batch = mnist.train.next_batch(batch_num)
            dropout_rate = nni.choice(1, 5, name='dropout_rate')
            mnist_network.train_step.run(feed_dict={mnist_network.images:
                batch[0], mnist_network.labels: batch[1], mnist_network.
                keep_prob: dropout_rate})
            if i % 100 == 0:
                test_acc = mnist_network.accuracy.eval(feed_dict={
                    mnist_network.images: mnist.test.images, mnist_network.
                    labels: mnist.test.labels, mnist_network.keep_prob: 1.0})
                nni.report_intermediate_result(test_acc)
                logger.debug('test accuracy %g', test_acc)
                logger.debug('Pipe send intermediate result done.')
        test_acc = mnist_network.accuracy.eval(feed_dict={mnist_network.
            images: mnist.test.images, mnist_network.labels: mnist.test.
            labels, mnist_network.keep_prob: 1.0})