def parse_params(self,
                   y=None,
                   y_target=None,
                   batch_size=1,
                   confidence=0,
                   learning_rate=5e-3,
                   binary_search_steps=5,
                   max_iterations=1000,
                   abort_early=True,
                   initial_const=1e-2,
                   clip_min=0,
                   clip_max=1):
    """
    :param y: (optional) A tensor with the true labels for an untargeted
              attack. If None (and y_target is None) then use the
              original labels the classifier assigns.
    :param y_target: (optional) A tensor with the target labels for a
              targeted attack.
    :param confidence: Confidence of adversarial examples: higher produces
                       examples with larger l2 distortion, but more
                       strongly classified as adversarial.
    :param batch_size: Number of attacks to run simultaneously.
    :param learning_rate: The learning rate for the attack algorithm.
                          Smaller values produce better results but are
                          slower to converge.
    :param binary_search_steps: The number of times we perform binary
                                search to find the optimal tradeoff-
                                constant between norm of the purturbation
                                and confidence of the classification.
    :param max_iterations: The maximum number of iterations. Setting this
                           to a larger value will produce lower distortion
                           results. Using only a few iterations requires
                           a larger learning rate, and will produce larger
                           distortion results.
    :param abort_early: If true, allows early aborts if gradient descent
                        is unable to make progress (i.e., gets stuck in
                        a local minimum).
    :param initial_const: The initial tradeoff-constant to use to tune the
                          relative importance of size of the perturbation
                          and confidence of classification.
                          If binary_search_steps is large, the initial
                          constant is not important. A smaller value of
                          this constant gives lower distortion results.
    :param clip_min: (optional float) Minimum input component value
    :param clip_max: (optional float) Maximum input component value
    """

    # ignore the y and y_target argument
    self.batch_size = batch_size
    self.confidence = confidence
    self.learning_rate = learning_rate
    self.binary_search_steps = binary_search_steps
    self.max_iterations = max_iterations
    self.abort_early = abort_early
    self.initial_const = initial_const
    self.clip_min = clip_min
    self.clip_max = clip_max
:param y: (optional) A tensor with the true labels for an untargeted
              attack. If None (and y_target is None) then use the
              original labels the classifier assigns.
    :param y_target: (optional) A tensor with the target labels for a
              targeted attack.
    :param confidence: Confidence of adversarial examples: higher produces
                       examples with larger l2 distortion, but more
                       strongly classified as adversarial.
    :param batch_size: Number of attacks to run simultaneously.
    :param learning_rate: The learning rate for the attack algorithm.
                          Smaller values produce better results but are
                          slower to converge.
    :param binary_search_steps: The number of times we perform binary
                                search to find the optimal tradeoff-
                                constant between norm of the purturbation
                                and confidence of the classification.
    :param max_iterations: The maximum number of iterations. Setting this
                           to a larger value will produce lower distortion
                           results. Using only a few iterations requires
                           a larger learning rate, and will produce larger
                           distortion results.
    :param abort_early: If true, allows early aborts if gradient descent
                        is unable to make progress (i.e., gets stuck in
                        a local minimum).
    :param initial_const: The initial tradeoff-constant to use to tune the
                          relative importance of size of the perturbation
                          and confidence of classification.
                          If binary_search_steps is large, the initial
                          constant is not important. A smaller value of
                          this constant gives lower distortion results.
    :param clip_min: (optional float) Minimum input component value
    :param clip_max: (optional float) Maximum input component value