code: def attack_step(self, x, y, modifier, const):
      x_new, grads, loss, preds, l2_dist = self.gradient(
          x, y, modifier, const
      )
  
      self.optimizer.apply_gradients([(grads, modifier)])
      return x_new, 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 4 for calling the CarliniWagnerL2 obj's underlying member method code with example usage: obj.attack_step(x, y, modifier, const) and returns: x_new,  loss,  preds,  l2_dist