human
Give me a the Python code you requested given the following:
Complete the below
def main():

    ''''MNIST CleverHans tutorial
    :return:'
    '''
assistant
#Here is the Python code you requested.
    if (not hasattr(backend, u'theano')):
        raise RuntimeError(u'This    tutorial    requires    keras    to    be    configured    to    use    the    Theano    backend.')
    if (keras.backend.image_dim_ordering() != u'th'):
        keras.backend.set_image_dim_ordering(u'th')
        print(u"INFO:    '~/.keras/keras.json'    sets    'image_dim_ordering'    to    'tf',    temporarily    setting    to    'th'")
    parser = argparse.ArgumentParser()
    parser.add_argument(u'--batch_size', u'-b', default=128, help=u'Size    of    training    batches')
    parser.add_argument(u'--train_dir', u'-d', default=u'/tmp', help=u'Directory    storing    the    saved    model.')
    parser.add_argument(u'--filename', u'-f', default=u'mnist.ckpt', help=u'Filename    to    save    model    under.')
    parser.add_argument(u'--nb_epochs', u'-e', default=6, type=int, help=u'Number    of    epochs    to    train    model')
    parser.add_argument(u'--learning_rate', u'-lr', default=0.5, type=float, help=u'Learning    rate    for    training')
    args = parser.parse_args()
    (X_train, Y_train, X_test, Y_test) = data_mnist()
    print(u'Loaded    MNIST    test    data.')
    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_shape = (None, 1, 28, 28)
    x = T.tensor4(u'x')
    y = T.matrix(u'y')
    model = cnn_model()
    model.build(x_shape)
    predictions = model(x)
    print(u'Defined    Theano    model    graph.')
    def evaluate():
        accuracy = th_model_eval(x, y, predictions, X_test, Y_test, args=args)
        assert (X_test.shape[0] == 10000), X_test.shape
        print((u'Test    accuracy    on    legitimate    test    examples:    ' + str(accuracy)))
        pass
    th_model_train(x, y, predictions, model.trainable_weights, X_train, Y_train, evaluate=evaluate, args=args)
    fgsm = FastGradientMethod(model, back=u'th')
    adv_x = fgsm.generate(x, params={u'eps': 0.3})
    accuracy = th_model_eval(x, y,...