code: def forward(self, perturbation, mu, adv_target):
      # mu: batch x prediction_length x dim
      mu = mu[:, self.attack_idx, :]  # batch x 1 x dim
  
      adv_target = adv_target[:, self.attack_idx, :]  # batch x 1 x dim
  
      loss_function = nn.MSELoss(reduction="none")
  
      distance_tensor = loss_function(adv_target, mu)[
          ..., self.target_items
      ].sum(
          -1
      )  # batch x len(attack_idx)
      distance_per_sample = distance_tensor.sum(axis=1)  # batch
      distance = distance_per_sample.sum()  # scalar
  
      zero = torch.zeros(perturbation.shape).to(self.device)
      norm_tensor = loss_function(perturbation, zero).sum(
          -1
      )  # batch x context_length
      norm_per_sample = norm_tensor.sum(axis=1)  # batch
      norm = norm_per_sample.sum()  # scalar
  
      loss_per_sample = distance_per_sample
      loss = loss_per_sample.sum()
  
      return (
          norm_per_sample,
          distance_per_sample,
          loss_per_sample,
          norm,
          distance,
          loss,
      )
  
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 AttackLoss obj's underlying member method code with example usage: obj.forward(perturbation, mu, adv_target) and returns: norm_per_sample,  distance_per_sample,  loss_per_sample,  norm,  distance,  loss