class_docstr: Weighted SoftmaxCrossEntropyLoss2D for ICNet training
  
  Parameters
  ----------
  weights : tuple, default (0.4, 0.4, 1.0)
      The weight for cascade label guidance.
  ignore_label : int, default -1
      The label to ignore.
class_docstr_tok: ["Weighted","SoftmaxCrossEntropyLoss2D","for","ICNet","training","Parameters","--","--","--","--","--","weights",":","tuple",",","default","(","0.4",",","0.4",",","1.0",")","The","weight","for","cascade","label","guidance",".","ignore_label",":","int",",","default","-1","The","label","to","ignore","."]
code: def __init__(
      self,
      weights=(0.4, 0.4, 1.0),
      height=None,
      width=None,
      crop_size=480,
      ignore_label=-1,
      **kwargs,
  ):
      super(ICNetLoss, self).__init__(
          ignore_label=ignore_label, **kwargs
      )
      self.weights = weights
      self.height = height if height is not None else crop_size
      self.width = width if width is not None else crop_size
  
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 ICNetLoss object from inherited base classes: SoftmaxCrossEntropyLoss with example: obj = ICNetLoss(weights, height, width, crop_size, ignore_label)