class_docstr: Initializer for the sum cross-entropy loss. For a single
  tensor, this is equivalent to the cross-entropy loss. For a
  list of tensors, this computes the sum of the cross-entropy
  losses for each tensor in the list against the target. Can accommodate
  target vectors, e.g. smoothed labels.
  
  Config params:
      weight: weight of sample, optional
      ignore_index: sample should be ignored for loss, optional
      reduction: specifies reduction to apply to the output, optional
      temperature: specify temperature for softmax. Default 1.0
      label_smoothing: specific a label smoothing factor between 0.0 and 1.0 (default is 0.0)
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docstr: For each output and single target, loss is calculated.
  The returned loss value is the sum loss across all outputs.
docstr_tok: ["For","each","output","and","single","target",",","loss","is","calculated",".","The","returned","loss","value","is","the","sum","loss","across","all","outputs","."]
code: def forward(
      self,
      output: Union[torch.Tensor, List[torch.Tensor]],
      target: torch.Tensor,
  ):
      """
      For each output and single target, loss is calculated.
      The returned loss value is the sum loss across all outputs.
      """
      return self.criterion(output, target)
  
imports: from dataclasses import dataclass
  from typing import List, Optional, Union
  import torch
  import torch.nn.functional
  from classy_vision.generic.util import is_on_gpu
  from classy_vision.losses import ClassyLoss, register_loss
  from torch import nn, Tensor
  from vissl.config import AttrDict
label_desc: Use image node_id 3 for calling the CrossEntropyMultipleOutputSingleTargetLoss obj's underlying member method code with example usage: obj.forward(output, target) and returns: self