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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def main(params):
    '''
    Main function, build mnist network, run and send result to NNI.
    '''
    # Import data
    mnist = download_mnist_retry(params['data_dir'])
    print('Mnist download data done.')
    logger.debug('Mnist download data done.')

    # Create the model
    # Build the graph for the deep net
    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)
                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.')