class_docstr: Cross entropy loss with soft target.
docstr: Args:
      reduction (str): specifies reduction to apply to the output.
          It can be "mean" (default) or "none".
docstr_tok: ["Args",":","reduction","(","str",")",":","specifies","reduction","to","apply","to","the","output",".","It","can","be","``","mean","''","(","default",")","or","``","none","''","."]
code: def __init__(self, reduction: str = "mean") -> None:
      """
      Args:
          reduction (str): specifies reduction to apply to the output.
              It can be "mean" (default) or "none".
      """
      super(SoftTargetCrossEntropy, self).__init__()
      self.reduction = reduction
  
imports: from typing import List
  import pytorchvideo_trainer.module.distributed_utils
  import torch
  import torch.nn
  import torch.nn.functional
  from pytorchvideo.layers.utils import set_attributes
label_desc: Use image node_id 1 to create a new SoftTargetCrossEntropy object from inherited base classes: nn with example: obj = SoftTargetCrossEntropy(reduction)