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","."]
code: def _mixup_forward(
      self, F, pred, label1, label2, lam, sample_weight=None
  ):
      if not self._from_logits:
          pred = F.log_softmax(pred, self._axis)
      if self._sparse_label:
          loss1 = -F.pick(pred, label1, axis=self._axis, keepdims=True)
          loss2 = -F.pick(pred, label2, axis=self._axis, keepdims=True)
          loss = lam * loss1 + (1 - lam) * loss2
      else:
          label1 = _reshape_like(F, label1, pred)
          label2 = _reshape_like(F, label2, pred)
          loss1 = -F.sum(pred * label1, axis=self._axis, keepdims=True)
          loss2 = -F.sum(pred * label2, axis=self._axis, keepdims=True)
          loss = lam * loss1 + (1 - lam) * loss2
      loss = _apply_weighting(F, loss, self._weight, sample_weight)
      return F.mean(loss, axis=self._batch_axis, exclude=True)
  
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 4 for calling the MixSoftmaxCrossEntropyLoss obj's underlying member method code with example usage: obj._mixup_forward(F, pred, label1, label2, lam, sample_weight) and returns: F