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):
    """
    :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
    """
    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
: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