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 _weighted_forward(self, F, *inputs):
      label = inputs[4]
      loss = []
      for i in range(len(inputs) - 1):
          scale_pred = F.contrib.BilinearResize2D(
              inputs[i], height=self.height, width=self.width
          )
          loss.append(
              super(ICNetLoss, self).hybrid_forward(
                  F, scale_pred, label
              )
          )
  
      return (
          loss[0]
          + self.weights[0] * loss[1]
          + self.weights[1] * loss[2]
          + self.weights[2] * loss[3]
      )
  
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 2 for calling the ICNetLoss obj's underlying member method code with example usage: obj._weighted_forward(F) and returns: unknown