class_docstr: SoftmaxCrossEntropyLoss with ignore labels
  
  Parameters
  ----------
  axis : int, default -1
      The axis to sum over when computing softmax and entropy.
  sparse_label : bool, default True
      Whether label is an integer array instead of probability distribution.
  from_logits : bool, default False
      Whether input is a log probability (usually from log_softmax) instead
      of unnormalized numbers.
  weight : float or None
      Global scalar weight for loss.
  batch_axis : int, default 0
      The axis that represents mini-batch.
  ignore_label : int, default -1
      The label to ignore.
  size_average : bool, default False
      Whether to re-scale loss with regard to ignored labels.
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code: def __init__(
      self,
      sparse_label=True,
      batch_axis=0,
      ignore_label=-1,
      size_average=True,
      **kwargs,
  ):
      super(SoftmaxCrossEntropyOHEMLoss, self).__init__(
          None, batch_axis, **kwargs
      )
      self._sparse_label = sparse_label
      self._ignore_label = ignore_label
      self._size_average = size_average
  
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 SoftmaxCrossEntropyOHEMLoss object from inherited base classes: Loss with example: obj = SoftmaxCrossEntropyOHEMLoss(sparse_label, batch_axis, ignore_label, size_average)