code: def accuracy(sess, model, x, y, batch_size=None, devices=None, feed=None,
               attack=None, attack_params=None):
    
  
    _check_x(x)
    _check_y(y)
    if x.shape[0] != y.shape[0]:
      raise ValueError("Number of input examples and labels do not match.")
  
    factory = _CorrectFactory(model, attack, attack_params)
  
    correct, = batch_eval_multi_worker(sess, factory, [x, y],
                                       batch_size=batch_size, devices=devices,
                                       feed=feed)
  
    return correct.mean()
docs: Compute the accuracy of a TF model on some data
    :param sess: TF session to use when training the graph
    :param model: cleverhans.model.Model instance
    :param x: numpy array containing input examples (e.g. MNIST().x_test )
    :param y: numpy array containing example labels (e.g. MNIST().y_test )
    :param batch_size: Number of examples to use in a single evaluation batch.
        If not specified, this function will use a reasonable guess and
        may run out of memory.
        When choosing the batch size, keep in mind that the batch will
        be divided up evenly among available devices. If you can fit 128
        examples in memory on one GPU and you have 8 GPUs, you probably
        want to use a batch size of 1024 (unless a different batch size
        runs faster with the ops you are using, etc.)
    :param devices: An optional list of string device names to use.
      If not specified, this function will use all visible GPUs.
    :param feed: An optional dictionary that is appended to the feeding
             dictionary before the session runs. Can be used to feed
             the learning phase of a Keras model for instance.
    :param attack: cleverhans.attack.Attack
      Optional. If no attack specified, evaluates the model on clean data.
      If attack is specified, evaluates the model on adversarial examples
      created by the attack.
    :param attack_params: dictionary
      If attack is specified, this dictionary is passed to attack.generate
      as keyword arguments.
    :return: a float with the accuracy value