code: def __init__(
      self, predict, base_adversaries, loss_fn=None, targeted=False
  ):
      self.predict = predict
      self.base_adversaries = base_adversaries
      self.loss_fn = loss_fn
      self.targeted = targeted
  
      if self.loss_fn is None:
          self.loss_fn = ZeroOneLoss(reduction="none")
      else:
          assert self.loss_fn.reduction == "none"
  
      for adversary in self.base_adversaries:
          assert self.targeted == adversary.targeted
  
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 1 to create a new ChooseBestAttack object from inherited base classes: Attack, LabelMixin with example: obj = ChooseBestAttack(predict, base_adversaries, loss_fn, targeted)