code: def gradient(self, x, y, modifier, const):
      # compute the actual attack
      with tf.GradientTape() as tape:
          adv_image = modifier + x
          x_new = clip_tanh(
              adv_image, clip_min=self.clip_min, clip_max=self.clip_max
          )
          preds = self.model_fn(x_new)
          loss, l2_dist = loss_fn(
              x=x,
              x_new=x_new,
              y_true=y,
              y_pred=preds,
              confidence=self.confidence,
              const=const,
              targeted=self.targeted,
              clip_min=self.clip_min,
              clip_max=self.clip_max,
          )
  
      grads = tape.gradient(loss, adv_image)
      return x_new, grads, loss, preds, l2_dist
  
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 5 for calling the CarliniWagnerL2 obj's underlying member method code with example usage: obj.gradient(x, y, modifier, const) and returns: x_new,  grads,  loss,  preds,  l2_dist