code: def soft_logit_margin_loss(
      logits, targets, reduction="elementwise_mean", offset=0.0
  ):
      batch_size = logits.size(0)
      num_class = logits.size(1)
      mask = torch.ones_like(logits).byte()
      # TODO: need to cover different versions of torch
      # mask = torch.ones_like(logits).bool()
      mask[torch.arange(batch_size), targets] = 0
      logits_true_label = logits[torch.arange(batch_size), targets]
      logits_other_label = logits[mask].reshape(
          batch_size, num_class - 1
      )
      loss = (
          torch.logsumexp(logits_other_label, dim=1) - logits_true_label
      )
      return _reduce_loss(loss, reduction) + offset
  
imports: import torch
  from torch.nn.modules.loss import _Loss
  from advertorch.utils import clamp
label_desc: Use image node_id 6 for calling a global function with example usage: soft_logit_margin_loss(logits, targets, reduction, offset) and returns: unknown