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
docstr: Take in a dictionary of parameters and applies attack-specific checks
  before saving them as attributes.
  
  Attack-specific parameters:
  
  :param eps: (optional float) maximum distortion of adversarial example
              compared to original input
  :param ord: (optional) Order of the norm (mimics Numpy).
              Possible values: np.inf
  :param clip_min: (optional float) Minimum input component value
  :param clip_max: (optional float) Maximum input component value
docstr_tok: ["Take","in","a","dictionary","of","parameters","and","applies","attack-specific","checks","before","saving","them","as","attributes",".","Attack-specific","parameters",":",":","param","eps",":","(","optional","float",")","maximum","distortion","of","adversarial","example","compared","to","original","input",":","param","ord",":","(","optional",")","Order","of","the","norm","(","mimics","Numpy",")",".","Possible","values",":","np.inf",":","param","clip_min",":","(","optional","float",")","Minimum","input","component","value",":","param","clip_max",":","(","optional","float",")","Maximum","input","component","value"]
code: def parse_params(
      self, eps=0.3, ord=np.inf, clip_min=None, clip_max=None, **kwargs
  ):
      """
      Take in a dictionary of parameters and applies attack-specific checks
      before saving them as attributes.
  
      Attack-specific parameters:
  
      :param eps: (optional float) maximum distortion of adversarial example
                  compared to original input
      :param ord: (optional) Order of the norm (mimics Numpy).
                  Possible values: np.inf
      :param clip_min: (optional float) Minimum input component value
      :param clip_max: (optional float) Maximum input component value
      """
  
      # Save attack-specific parameters
      self.eps = eps
      self.ord = ord
      self.clip_min = clip_min
      self.clip_max = clip_max
  
      # Check if order of the norm is acceptable given current implementation
      if self.ord not in [np.inf]:
          raise ValueError("Norm order must be np.inf")
      if len(kwargs.keys()) > 0:
          warnings.warn(
              "kwargs is unused and will be removed on or after "
              "2019-04-26."
          )
  
      return True
  
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 3 for calling the Noise obj's underlying member method code with example usage: obj.parse_params(eps, ord, clip_min, clip_max) and returns: True