code: def perturb(self, x, y=None):
      # TODO: might want to also retain the list of all attacks
  
      x, y = self._verify_and_process_inputs(x, y)
  
      with torch.no_grad():
          maxloss = self.loss_fn(self.predict(x), y)
      final_adv = torch.zeros_like(x)
      for adversary in self.base_adversaries:
          adv = adversary.perturb(x, y)
          loss = self.loss_fn(self.predict(adv), y)
          to_replace = maxloss < loss
          final_adv[to_replace] = adv[to_replace]
          maxloss[to_replace] = loss[to_replace]
  
      return final_adv
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import collections
  import numpy
  import torch
  from torch.distributions import laplace
  from torch.distributions import uniform
  from torch.nn.modules.loss import _Loss
  from advertorch.utils import clamp
  from advertorch.utils import clamp_by_pnorm
  from advertorch.utils import batch_multiply
  from advertorch.utils import normalize_by_pnorm
  from advertorch.utils import predict_from_logits
  from advertorch.loss import ZeroOneLoss
  from advertorch.attacks import Attack, LabelMixin
label_desc: Use image node_id 2 for calling the ChooseBestAttack obj's underlying member method code with example usage: obj.perturb(x, y) and returns: final_adv