code: def attack_step(
      self, attack_module, optimizer, adv_target, ground_truth=None
  ):
      optimizer.zero_grad()
  
      perturbed_mu = attack_module()
      batch_size = perturbed_mu.shape[0]
      max_norm = self.max_norm
  
      # targeted loss
      _, _, _, _, _, loss = attack_module.attack_loss(
          attack_module.get_perturbation(), perturbed_mu, adv_target
      )
  
      # backward pass
      loss.backward()
  
      # renorm grad
      grad_norm = (
          attack_module.get_grad().view(batch_size, -1).norm(p=2, dim=1)
      )
      attack_module.perturbation.grad.div_(
          grad_norm.view(batch_size, 1, 1)
      )
  
      # back-propogation
      optimizer.step()
      attack_module.perturbation.data.clamp_(
          min=-max_norm, max=max_norm
      )
  
imports: import torch
  import torch.nn
  import torch.optim
  from tqdm.auto import tqdm
  from typing import List
  from sparse_layer import SparseNet
label_desc: Use image node_id 2 for calling the Attack obj's underlying member method code with example usage: obj.attack_step(attack_module, optimizer, adv_target, ground_truth) without return types