class_docstr: LBFGS is the first adversarial attack for convolutional neural networks,
  and is a target & iterative attack.
  Paper link: "https://arxiv.org/pdf/1312.6199.pdf"
  :param model: cleverhans.model.Model
  :param sess: tf.Session
  :param dtypestr: dtype of the data
  :param kwargs: passed through to super constructor
docstr: :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
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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,
  ):
      """
      :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
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans/attacks/lbfgs.py
imports: import numpy
  import tensorflow
  from cleverhans.attacks.attack import Attack
  from cleverhans.compat import reduce_sum, softmax_cross_entropy_with_logits
  from cleverhans.model import CallableModelWrapper, Model, wrapper_warning
  from cleverhans import utils
  from cleverhans import utils_tf
label_desc: Use image node_id 3 for calling the LBFGS obj's underlying member method code with example usage: obj.parse_params(y_target, batch_size, binary_search_steps, max_iterations, initial_const, clip_min, clip_max) without return types