code: def forward(
      self, logits, targets
  ):  # pylint: disable=arguments-differ
      assert logits.shape[-1] >= 2
      top_logits, top_classes = torch.topk(logits, 2, dim=-1)
      target_logits = logits[torch.arange(logits.shape[0]), targets]
      max_nontarget_logits = torch.where(
          top_classes[..., 0] == targets,
          top_logits[..., 1],
          top_logits[..., 0],
      )
  
      loss = max_nontarget_logits - target_logits
      if self.reduction == "none":
          pass
      elif self.reduction == "sum":
          loss = loss.sum()
      elif self.reduction == "mean":
          loss = loss.mean()
      else:
          raise ValueError("unknown reduction: '%s'" % (self.recution,))
  
      return loss
  
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 for calling the MarginalLoss obj's underlying member method code with example usage: obj.forward(logits, targets) and returns: loss