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: Generate symbolic graph for adversarial examples and return.
  
  :param x: The model's symbolic inputs.
  :param kwargs: See `parse_params`
docstr_tok: ["Generate","symbolic","graph","for","adversarial","examples","and","return",".",":","param","x",":","The","model","'s","symbolic","inputs",".",":","param","kwargs",":","See","`","parse_params","`"]
code: def generate(self, x, **kwargs):
      """
      Generate symbolic graph for adversarial examples and return.
  
      :param x: The model's symbolic inputs.
      :param kwargs: See `parse_params`
      """
      # Parse and save attack-specific parameters
      assert self.parse_params(**kwargs)
  
      if self.ord != np.inf:
          raise NotImplementedError(self.ord)
      eta = tf.random_uniform(
          tf.shape(x), -self.eps, self.eps, dtype=self.tf_dtype
      )
      adv_x = x + eta
      if self.clip_min is not None or self.clip_max is not None:
          assert self.clip_min is not None and self.clip_max is not None
          adv_x = tf.clip_by_value(adv_x, self.clip_min, self.clip_max)
  
      return adv_x
  
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 2 for calling the Noise obj's underlying member method code with example usage: obj.generate(x) and returns: adv_x