code: def forward(self, x, target):
      if self.training:
          x = x.float()
          target = target.float()
          logprobs = torch.nn.functional.log_softmax(x, dim=-1)
  
          nll_loss = -logprobs * target
          nll_loss = nll_loss.sum(-1)
  
          smooth_loss = -logprobs.mean(dim=-1)
  
          loss = (
              self.confidence * nll_loss + self.smoothing * smooth_loss
          )
  
          return loss.mean()
      else:
          return torch.nn.functional.cross_entropy(x, target)
  
file_path: DALI/docs/examples/use_cases/pytorch/efficientnet/image_classification/mixup.py
imports: import torch
  import torch.nn
  import numpy
label_desc: Use image node_id 2 for calling the NLLMultiLabelSmooth obj's underlying member method code with example usage: obj.forward(x, target) and returns: loss, torch