class_docstr: SoftmaxCrossEntropyLoss2D with Auxiliary Loss
  
  Parameters
  ----------
  aux : bool, default True
      Whether to use auxiliary loss.
  aux_weight : float, default 0.2
      The weight for aux loss.
  ignore_label : int, default -1
      The label to ignore.
class_docstr_tok: ["SoftmaxCrossEntropyLoss2D","with","Auxiliary","Loss","Parameters","--","--","--","--","--","aux",":","bool",",","default","True","Whether","to","use","auxiliary","loss",".","aux_weight",":","float",",","default","0.2","The","weight","for","aux","loss",".","ignore_label",":","int",",","default","-1","The","label","to","ignore","."]
docstr: Compute loss
docstr_tok: ["Compute","loss"]
code: def hybrid_forward(self, F, *inputs, **kwargs):
      """Compute loss"""
      if self.aux:
          if self.mixup:
              return self._aux_mixup_forward(F, *inputs, **kwargs)
          else:
              return self._aux_forward(F, *inputs, **kwargs)
      else:
          if self.mixup:
              return self._mixup_forward(F, *inputs, **kwargs)
          else:
              return super(
                  MixSoftmaxCrossEntropyLoss, self
              ).hybrid_forward(F, *inputs, **kwargs)
  
imports: from __future__ import absolute_import
  from mxnet import gluon
  from mxnet import nd
  from mxnet.gluon.loss import Loss, _apply_weighting, _reshape_like
label_desc: Use image node_id 5 for calling the MixSoftmaxCrossEntropyLoss obj's underlying member method code with example usage: obj.hybrid_forward(F) and returns: self, self, self, super