class_docstr: 2D Cross Entropy Loss with Multi-Loss
class_docstr_tok: ["2D","Cross","Entropy","Loss","with","Multi-Loss"]
code: def hybrid_forward(self, F, *inputs, **kwargs):
      pred1, pred2, pred3, label = tuple(inputs)
  
      loss1 = super(SegmentationMultiLosses, self).hybrid_forward(
          F, pred1, label, **kwargs
      )
      loss2 = super(SegmentationMultiLosses, self).hybrid_forward(
          F, pred2, label, **kwargs
      )
      loss3 = super(SegmentationMultiLosses, self).hybrid_forward(
          F, pred3, label, **kwargs
      )
      loss = loss1 + loss2 + loss3
      return loss
  
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 SegmentationMultiLosses obj's underlying member method code with example usage: obj.hybrid_forward(F) and returns: loss