class_docstr: A weak attack that just picks a random point in the attacker's action space.
  When combined with an attack bundling function, this can be used to implement
  random search.
  
  References:
  https://arxiv.org/abs/1802.00420 recommends random search to help identify
    gradient masking.
  https://openreview.net/forum?id=H1g0piA9tQ recommends using noise as part
    of an attack bundling recipe combining many different optimizers to yield
    a stronger optimizer.
  
  :param model: cleverhans.model.Model
  :param sess: optional tf.Session
  :param dtypestr: dtype of the data
  :param kwargs: passed through to super constructor
code: def __init__(self, model, sess=None, dtypestr="float32", **kwargs):
      super(Noise, self).__init__(
          model, sess=sess, dtypestr=dtypestr, **kwargs
      )
      self.feedable_kwargs = ("eps", "clip_min", "clip_max")
      self.structural_kwargs = ["ord"]
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans/attacks/noise.py
imports: import warnings
  import numpy
  import tensorflow
  from cleverhans.attacks.attack import Attack
label_desc: Use image node_id 1 to create a new Noise object from inherited base classes: Attack with example: obj = Noise(model, sess, dtypestr)