class_docstr: Drop-in replacement for nn.CrossEntropyLoss with few additions:
  - Support of label smoothing
code: def __init__(
      self,
      reduction: str = "mean",
      smooth_factor: float = 0.0,
      ignore_index: Optional[int] = -100,
      dim=1,
  ):
      super().__init__()
      self.smooth_factor = smooth_factor
      self.ignore_index = ignore_index
      self.reduction = reduction
      self.dim = 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 1 to create a new SoftCrossEntropyLoss object from inherited base classes: nn with example: obj = SoftCrossEntropyLoss(reduction, smooth_factor, ignore_index, dim)