code: def parse_params(self,
                     y_target=None,
                     batch_size=1,
                     binary_search_steps=5,
                     max_iterations=1000,
                     initial_const=1e-2,
                     clip_min=0,
                     clip_max=1):
      
      self.y_target = y_target
      self.batch_size = batch_size
      self.binary_search_steps = binary_search_steps
      self.max_iterations = max_iterations
      self.initial_const = initial_const
      self.clip_min = clip_min
      self.clip_max = clip_max
docs: :param y_target: (optional) A tensor with the one-hot target labels.
      :param batch_size: The number of inputs to include in a batch and
                         process simultaneously.
      :param binary_search_steps: The number of times we perform binary
                                  search to find the optimal tradeoff-
                                  constant between norm of the purturbation
                                  and cross-entropy loss of classification.
      :param max_iterations: The maximum number of iterations.
      :param initial_const: The initial tradeoff-constant to use to tune the
                            relative importance of size of the perturbation
                            and cross-entropy loss of the classification.
      :param clip_min: (optional float) Minimum input component value
      :param clip_max: (optional float) Maximum input component value