How to use the cleverhans.compat.flags.DEFINE_integer function in cleverhans

"""
  from cleverhans_tutorials import check_installation
  check_installation(__file__)

  mnist_tutorial(nb_epochs=FLAGS.nb_epochs, batch_size=FLAGS.batch_size,
                 learning_rate=FLAGS.learning_rate,
                 clean_train=FLAGS.clean_train,
                 backprop_through_attack=FLAGS.backprop_through_attack,
                 nb_filters=FLAGS.nb_filters)


if __name__ == '__main__':
  flags.DEFINE_integer('nb_filters', NB_FILTERS, 'Model size multiplier')
  flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
                       'Number of epochs to train model')
  flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
  flags.DEFINE_float('learning_rate', LEARNING_RATE,
                     'Learning rate for training')
  flags.DEFINE_bool('clean_train', CLEAN_TRAIN, 'Train on clean examples')
  flags.DEFINE_bool('backprop_through_attack', BACKPROP_THROUGH_ATTACK,
                    ('If True, backprop through adversarial example '
                     'construction process during adversarial training'))

  tf.app.run()

from cleverhans_tutorials import check_installation
  check_installation(__file__)

  cifar10_tutorial(nb_epochs=FLAGS.nb_epochs, batch_size=FLAGS.batch_size,
                   learning_rate=FLAGS.learning_rate,
                   clean_train=FLAGS.clean_train,
                   backprop_through_attack=FLAGS.backprop_through_attack,
                   nb_filters=FLAGS.nb_filters)


if __name__ == '__main__':
  flags.DEFINE_integer('nb_filters', NB_FILTERS,
                       'Model size multiplier')
  flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
                       'Number of epochs to train model')
  flags.DEFINE_integer('batch_size', BATCH_SIZE,
                       'Size of training batches')
  flags.DEFINE_float('learning_rate', LEARNING_RATE,
                     'Learning rate for training')
  flags.DEFINE_bool('clean_train', CLEAN_TRAIN, 'Train on clean examples')
  flags.DEFINE_bool('backprop_through_attack', BACKPROP_THROUGH_ATTACK,
                    ('If True, backprop through adversarial example '
                     'construction process during adversarial training'))

  tf.app.run()

flags.DEFINE_boolean('save', True,
                       'Whether to save from a checkpoint.')
  flags.DEFINE_string('save_dir', 'runs/X',
                      'Location to store logs/model.')
  flags.DEFINE_string('model_type', 'madry',
                      'Model type: basic|madry|resnet_tf.')
  flags.DEFINE_string('attack_type_train', 'MadryEtAl_y_multigpu',
                      'Attack type for adversarial training:\
                        FGSM|MadryEtAl{,_y}{,_multigpu}.')
  flags.DEFINE_string('attack_type_test', 'FGSM',
                      'Attack type for test: FGSM|MadryEtAl{,_y}.')
  flags.DEFINE_string('dataset', 'mnist', 'Dataset mnist|cifar10.')
  flags.DEFINE_boolean('only_adv_train', False,
                       'Do not train with clean examples when adv training.')
  flags.DEFINE_integer('save_steps', 50, 'Save model per X steps.')
  flags.DEFINE_integer('attack_nb_iter_train', None,
                       'Number of iterations of training attack.')
  flags.DEFINE_integer('eval_iters', 1, 'Evaluate every X steps.')
  flags.DEFINE_integer('lrn_step', 30000, 'Step to decrease learning rate'
                       'for ResNet.')
  flags.DEFINE_float('adam_lrn', 0.001, 'Learning rate for Adam Optimizer.')
  flags.DEFINE_float('mom_lrn', 0.1,
                     'Learning rate for Momentum Optimizer.')
  flags.DEFINE_integer('ngpu', 1, 'Number of gpus.')
  flags.DEFINE_integer('sync_step', 1, 'Sync params frequency.')
  flags.DEFINE_boolean('fast_tests', False, 'Fast tests against attacks.')
  flags.DEFINE_string('data_path', './datasets/', 'Path to datasets.'
                      'Each dataset should be in a subdirectory.')

  app.run()

def main(argv=None):
  from cleverhans_tutorials import check_installation
  check_installation(__file__)

  mnist_tutorial(nb_epochs=FLAGS.nb_epochs,
                 batch_size=FLAGS.batch_size,
                 learning_rate=FLAGS.learning_rate,
                 train_dir=FLAGS.train_dir,
                 filename=FLAGS.filename,
                 load_model=FLAGS.load_model)


if __name__ == '__main__':
  flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
                       'Number of epochs to train model')
  flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
  flags.DEFINE_float('learning_rate', LEARNING_RATE,
                     'Learning rate for training')
  flags.DEFINE_string('train_dir', TRAIN_DIR,
                      'Directory where to save model.')
  flags.DEFINE_string('filename', FILENAME, 'Checkpoint filename.')
  flags.DEFINE_boolean('load_model', LOAD_MODEL,
                       'Load saved model or train.')
  tf.app.run()

flags.DEFINE_string('attack_type_test', 'FGSM',
                      'Attack type for test: FGSM|MadryEtAl{,_y}.')
  flags.DEFINE_string('dataset', 'mnist', 'Dataset mnist|cifar10.')
  flags.DEFINE_boolean('only_adv_train', False,
                       'Do not train with clean examples when adv training.')
  flags.DEFINE_integer('save_steps', 50, 'Save model per X steps.')
  flags.DEFINE_integer('attack_nb_iter_train', None,
                       'Number of iterations of training attack.')
  flags.DEFINE_integer('eval_iters', 1, 'Evaluate every X steps.')
  flags.DEFINE_integer('lrn_step', 30000, 'Step to decrease learning rate'
                       'for ResNet.')
  flags.DEFINE_float('adam_lrn', 0.001, 'Learning rate for Adam Optimizer.')
  flags.DEFINE_float('mom_lrn', 0.1,
                     'Learning rate for Momentum Optimizer.')
  flags.DEFINE_integer('ngpu', 1, 'Number of gpus.')
  flags.DEFINE_integer('sync_step', 1, 'Sync params frequency.')
  flags.DEFINE_boolean('fast_tests', False, 'Fast tests against attacks.')
  flags.DEFINE_string('data_path', './datasets/', 'Path to datasets.'
                      'Each dataset should be in a subdirectory.')

  app.run()

check_installation(__file__)

  mnist_tutorial_cw(viz_enabled=FLAGS.viz_enabled,
                    nb_epochs=FLAGS.nb_epochs,
                    batch_size=FLAGS.batch_size,
                    source_samples=FLAGS.source_samples,
                    learning_rate=FLAGS.learning_rate,
                    attack_iterations=FLAGS.attack_iterations,
                    model_path=FLAGS.model_path,
                    targeted=FLAGS.targeted)


if __name__ == '__main__':
  flags.DEFINE_boolean('viz_enabled', VIZ_ENABLED,
                       'Visualize adversarial ex.')
  flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
                       'Number of epochs to train model')
  flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
  flags.DEFINE_integer('source_samples', SOURCE_SAMPLES,
                       'Number of test inputs to attack')
  flags.DEFINE_float('learning_rate', LEARNING_RATE,
                     'Learning rate for training')
  flags.DEFINE_string('model_path', MODEL_PATH,
                      'Path to save or load the model file')
  flags.DEFINE_integer('attack_iterations', ATTACK_ITERATIONS,
                       'Number of iterations to run attack; 1000 is good')
  flags.DEFINE_boolean('targeted', TARGETED,
                       'Run the tutorial in targeted mode?')

  tf.app.run()

suffix = "_bundled_examples_report.joblib"
    assert model_filepath.endswith('.joblib')
    report_path = model_filepath[:-len('.joblib')] + suffix

  goal = MaxConfidence()
  bundle_examples_with_goal(sess, model, adv_x_list, y, goal,
                            report_path, batch_size=FLAGS.batch_size)


if __name__ == '__main__':
  flags.DEFINE_string('report_path', None, 'Report path')
  flags.DEFINE_integer('train_start', TRAIN_START, 'Starting point (inclusive)'
                       'of range of train examples to use')
  flags.DEFINE_integer('train_end', TRAIN_END, 'Ending point (non-inclusive) '
                       'of range of train examples to use')
  flags.DEFINE_integer('test_start', TEST_START, 'Starting point '
                       '(inclusive) of range of test examples to use')
  flags.DEFINE_integer('test_end', TEST_END, 'End point (non-inclusive) of '
                       'range of test examples to use')
  flags.DEFINE_string('which_set', WHICH_SET, '"train" or "test"')
  flags.DEFINE_integer('batch_size', BATCH_SIZE, 'batch size')
  tf.app.run()

except ValueError:
    raise ValueError(argv)
  print_accuracies(filepath=filepath, test_start=FLAGS.test_start,
                   test_end=FLAGS.test_end, which_set=FLAGS.which_set,
                   nb_iter=FLAGS.nb_iter, base_eps_iter=FLAGS.base_eps_iter,
                   batch_size=FLAGS.batch_size)


if __name__ == '__main__':
  flags.DEFINE_integer('train_start', TRAIN_START, 'Starting point (inclusive)'
                       'of range of train examples to use')
  flags.DEFINE_integer('train_end', TRAIN_END, 'Ending point (non-inclusive) '
                       'of range of train examples to use')
  flags.DEFINE_integer('test_start', TEST_START, 'Starting point (inclusive) '
                       'of range of test examples to use')
  flags.DEFINE_integer('test_end', TEST_END, 'End point (non-inclusive) of '
                       'range of test examples to use')
  flags.DEFINE_integer('nb_iter', NB_ITER, 'Number of iterations of PGD')
  flags.DEFINE_string('which_set', WHICH_SET, '"train" or "test"')
  flags.DEFINE_integer('batch_size', BATCH_SIZE,
                       'Batch size for most jobs')
  flags.DEFINE_float('base_eps_iter', BASE_EPS_ITER,
                     'epsilon per iteration, if data were in [0, 1]')
  tf.app.run()

'Number of classes in problem')
  flags.DEFINE_integer('batch_size', BATCH_SIZE,
                       'Size of training batches')
  flags.DEFINE_float('learning_rate', LEARNING_RATE,
                     'Learning rate for training')

  # Flags related to oracle
  flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
                       'Number of epochs to train model')

  # Flags related to substitute
  flags.DEFINE_integer('holdout', HOLDOUT,
                       'Test set holdout for adversary')
  flags.DEFINE_integer('data_aug', DATA_AUG,
                       'Number of substitute data augmentations')
  flags.DEFINE_integer('nb_epochs_s', NB_EPOCHS_S,
                       'Training epochs for substitute')
  flags.DEFINE_float('lmbda', LMBDA, 'Lambda from arxiv.org/abs/1602.02697')
  flags.DEFINE_integer('data_aug_batch_size', AUG_BATCH_SIZE,
                       'Batch size for augmentation')

  tf.app.run()

report_path = model_filepath[:-len('.joblib')] + suffix

  goal = MaxConfidence()
  bundle_examples_with_goal(sess, model, adv_x_list, y, goal,
                            report_path, batch_size=FLAGS.batch_size)


if __name__ == '__main__':
  flags.DEFINE_string('report_path', None, 'Report path')
  flags.DEFINE_integer('train_start', TRAIN_START, 'Starting point (inclusive)'
                       'of range of train examples to use')
  flags.DEFINE_integer('train_end', TRAIN_END, 'Ending point (non-inclusive) '
                       'of range of train examples to use')
  flags.DEFINE_integer('test_start', TEST_START, 'Starting point '
                       '(inclusive) of range of test examples to use')
  flags.DEFINE_integer('test_end', TEST_END, 'End point (non-inclusive) of '
                       'range of test examples to use')
  flags.DEFINE_string('which_set', WHICH_SET, '"train" or "test"')
  flags.DEFINE_integer('batch_size', BATCH_SIZE, 'batch size')
  tf.app.run()