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Complete this python program:
def main(argv=None):
assistant
#Here is the Python program you requested.

    """CIFAR10 CleverHans tutorial
    :return:
    """
    tf.set_random_seed(1234)
    if (not hasattr(backend, u'tf')):
        raise RuntimeError(u'This    tutorial    requires    keras    to    be    configured    to    use    the    TensorFlow    backend.')
    if (keras.backend.image_dim_ordering() != u'tf'):
        keras.backend.set_image_dim_ordering(u'tf')
        print(u"INFO:    '~/.keras/keras.json'    sets    'image_dim_ordering'    to    'th',    temporarily    setting    to    'tf'")
    sess = tf.Session()
    keras.backend.set_session(sess)
    (X_train, Y_train, X_test, Y_test) = data_cifar10()
    assert (Y_train.shape[1] == 10.0)
    label_smooth = 0.1
    Y_train = Y_train.clip((label_smooth / 9.0), (1.0 - label_smooth))
    x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))
    y = tf.placeholder(tf.float32, shape=(None, 10))
    model = cnn_model(img_rows=32, img_cols=32, channels=3)
    predictions = model(x)
    print(u'Defined    TensorFlow    model    graph.')
    def evaluate():
        eval_params = {u'batch_size': FLAGS.batch_size}
        accuracy = model_eval(sess, x, y, predictions, X_test, Y_test, args=eval_params)
        assert (X_test.shape[0] == 10000), X_test.shape
        print((u'Test    accuracy    on    legitimate    test    examples:    ' + str(accuracy)))
    train_params = {u'nb_epochs': FLAGS.nb_epochs, u'batch_size': FLAGS.batch_size, u'learning_rate': FLAGS.learning_rate}
    model_train(sess, x, y, predictions, X_train, Y_train, evaluate=evaluate, args=train_params)
    adv_x = fgsm(x, predictions, eps=0.3)
    eval_params = {u'batch_size': FLAGS.batch_size}
    (X_test_adv,) = batch_eval(sess, [x], [adv_x], [X_test], args=eval_params)
    assert (X_test_adv.shape[0] == 10000), X_test_adv.shape
    accuracy = model_eval(sess, x, y, predictions, X_test_adv, Y_test, args=eval_params)
    print((u'Test    accuracy    on    adversarial    examples:    ' + str(accuracy)))
    print(u'Repeating    the    process,    using    adversarial    training')
    model_2 = cnn_model(img_rows=32, img_cols=32, channels=3)
  ...