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","."]
docstr: Compute loss
docstr_tok: ["Compute","loss"]
code: def hybrid_forward(self, F, *inputs):
      """Compute loss"""
      return self._weighted_forward(F, *inputs)
  
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 3 for calling the ICNetLoss obj's underlying member method code with example usage: obj.hybrid_forward(F) and returns: self