class_docstr: Drop-in replacement for nn.CrossEntropyLoss with few additions:
  - Support of label smoothing
code: def forward(self, input: Tensor, target: Tensor) -> Tensor:
      log_prob = F.log_softmax(input, dim=self.dim)
      return label_smoothed_nll_loss(
          log_prob,
          target,
          epsilon=self.smooth_factor,
          ignore_index=self.ignore_index,
          reduction=self.reduction,
          dim=self.dim,
      )
  
imports: from typing import Optional
  from torch import nn, Tensor
  import torch.nn.functional
  from .functional import label_smoothed_nll_loss
label_desc: Use image node_id 2 for calling the SoftCrossEntropyLoss obj's underlying member method code with example usage: obj.forward(input, target) and returns: label_smoothed_nll_loss