class_docstr: SoftmaxCrossEntrolyLoss with Teacher model prediction
  
  Parameters
  ----------
  temperature : float, default 1
      The temperature parameter to soften teacher prediction.
  hard_weight : float, default 0.5
      The weight for loss on the one-hot label.
  sparse_label : bool, default True
      Whether the one-hot label is sparse.
class_docstr_tok: ["SoftmaxCrossEntrolyLoss","with","Teacher","model","prediction","Parameters","--","--","--","--","--","temperature",":","float",",","default","1","The","temperature","parameter","to","soften","teacher","prediction",".","hard_weight",":","float",",","default","0.5","The","weight","for","loss","on","the","one-hot","label",".","sparse_label",":","bool",",","default","True","Whether","the","one-hot","label","is","sparse","."]
code: def __init__(
      self, temperature=1, hard_weight=0.5, sparse_label=True, **kwargs
  ):
      super(DistillationSoftmaxCrossEntropyLoss, self).__init__(
          **kwargs
      )
      self._temperature = temperature
      self._hard_weight = hard_weight
      with self.name_scope():
          self.soft_loss = gluon.loss.SoftmaxCrossEntropyLoss(
              sparse_label=False, **kwargs
          )
          self.hard_loss = gluon.loss.SoftmaxCrossEntropyLoss(
              sparse_label=sparse_label, **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 1 to create a new DistillationSoftmaxCrossEntropyLoss object from inherited base classes: gluon with example: obj = DistillationSoftmaxCrossEntropyLoss(temperature, hard_weight, sparse_label)