code: def _attack(self, x):
      if self.clip_min is not None:
          if not np.all(tf.math.greater_equal(x, self.clip_min)):
              raise CarliniWagnerL2Exception(
                  f"The input is smaller than the minimum value of {self.clip_min}r"
              )
  
      if self.clip_max is not None:
          if not np.all(tf.math.less_equal(x, self.clip_max)):
              raise CarliniWagnerL2Exception(
                  f"The input is greater than the maximum value of {self.clip_max}!"
              )
  
      # cast to tensor if provided as numpy array
      original_x = tf.cast(x, tf.float32)
      shape = original_x.shape
  
      y, _ = get_or_guess_labels(
          self.model_fn, original_x, y=self.y, targeted=self.targeted
      )
  
      if not y.shape.as_list()[0] == original_x.shape.as_list()[0]:
          raise CarliniWagnerL2Exception(
              "x and y do not have the same shape!"
          )
  
      # re-scale x to [0, 1]
      x = original_x
      x = (x - self.clip_min) / (self.clip_max - self.clip_min)
      x = tf.clip_by_value(x, 0.0, 1.0)
  
      # scale to [-1, 1]
      x = (x * 2.0) - 1.0
  
      # convert tonh-space
      x = tf.atanh(x * 0.999999)
  
      # parameters for the binary search
      lower_bound = tf.zeros(shape[:1])
      upper_bound = tf.ones(shape[:1]) * 1e10
  
      const = tf.ones(shape) * self.initial_const
  
      # placeholder variables for best values
      best_l2 = tf.fill(shape[:1], 1e10)
      best_score = tf.fill(shape[:1], -1)
      best_score = tf.cast(best_score, tf.int32)
      best_attack = original_x
  
      # convience function for comparing
      compare_fn = tf.equal if self.targeted else tf.not_equal
  
      # the perturbation
      modifier = tf.Variable(
          tf.zeros(shape, dtype=x.dtype), trainable=True
      )
  
      for outer_step in range(self.binary_search_steps):
          # at each iteration reset variable state
          modifier.assign(tf.zeros(shape, dtype=x.dtype))
          for var in self.optimizer.variables():
              var.assign(tf.zeros(var.shape, dtype=var.dtype))
  
          # variables to keep track in the inner loop
          current_best_l2 = tf.fill(shape[:1], 1e10)
          current_best_score = tf.fill(shape[:1], -1)
          current_best_score = tf.cast(current_best_score, tf.int32)
  
          # The last iteration (if we run many steps) repeat the search once.
          if (
              self.binary_search_steps >= 10
              and outer_step == self.binary_search_steps - 1
          ):
              const = upper_bound
  
          # early stopping criteria
          prev = None
  
          for iteration in range(self.max_iterations):
              x_new, loss, preds, l2_dist = self.attack_step(
                  x, y, modifier, const
              )
  
              # check if we made progress, abort otherwise
              if (
                  self.abort_early
                  and iteration % ((self.max_iterations // 10) or 1)
                  == 0
              ):
                  if prev is not None and loss > prev * 0.9999:
                      break
  
                  prev = loss
  
              lab = tf.argmax(y, axis=1)
  
              pred_with_conf = (
                  preds - self.confidence
                  if self.targeted
                  else preds + self.confidence
              )
              pred_with_conf = tf.argmax(pred_with_conf, axis=1)
  
              pred = tf.argmax(preds, axis=1)
              pred = tf.cast(pred, tf.int32)
  
              # compute a binary mask of the tensors we want to assign
              mask = tf.math.logical_and(
                  tf.less(l2_dist, current_best_l2),
                  compare_fn(pred_with_conf, lab),
              )
  
              # all entries which evaluate to True get reassigned
              current_best_l2 = set_with_mask(
                  current_best_l2, l2_dist, mask
              )
              current_best_score = set_with_mask(
                  current_best_score, pred, mask
              )
  
              # if the l2 distance is better than the one found before
              # and if the example is a correct example (with regards to the labels)
              mask = tf.math.logical_and(
                  tf.less(l2_dist, best_l2),
                  compare_fn(pred_with_conf, lab),
              )
  
              best_l2 = set_with_mask(best_l2, l2_dist, mask)
              best_score = set_with_mask(best_score, pred, mask)
  
              # mask is of shape [batch_size]; best_attack is [batch_size, image_size]
              # need to expand
              mask = tf.reshape(mask, [-1, 1, 1, 1])
              mask = tf.tile(mask, [1, *best_attack.shape[1:]])
  
              best_attack = set_with_mask(best_attack, x_new, mask)
  
          # adjust binary search parameters
          lab = tf.argmax(y, axis=1)
          lab = tf.cast(lab, tf.int32)
  
          # we first compute the mask for the upper bound
          upper_mask = tf.math.logical_and(
              compare_fn(best_score, lab),
              tf.not_equal(best_score, -1),
          )
          upper_bound = set_with_mask(
              upper_bound,
              tf.math.minimum(upper_bound, const),
              upper_mask,
          )
  
          # based on this mask compute const mask
          const_mask = tf.math.logical_and(
              upper_mask,
              tf.less(upper_bound, 1e9),
          )
          const = set_with_mask(
              const, (lower_bound + upper_bound) / 2.0, const_mask
          )
  
          # else case is the negation of the inital mask
          lower_mask = tf.math.logical_not(upper_mask)
          lower_bound = set_with_mask(
              lower_bound,
              tf.math.maximum(lower_bound, const),
              lower_mask,
          )
  
          const_mask = tf.math.logical_and(
              lower_mask,
              tf.less(upper_bound, 1e9),
          )
          const = set_with_mask(
              const, (lower_bound + upper_bound) / 2, const_mask
          )
  
          const_mask = tf.math.logical_not(const_mask)
          const = set_with_mask(const, const * 10, const_mask)
  
      return best_attack
  
file_path: cleverhans/cleverhans/tf2/attacks/carlini_wagner_l2.py
imports: import numpy
  import tensorflow
  from cleverhans.tf2.utils import get_or_guess_labels, set_with_mask
label_desc: Use image node_id 3 for calling the CarliniWagnerL2 obj's underlying member method code with example usage: obj._attack(x) and returns: best_attack