class_docstr: Fast-SCNN: Fast Semantic Segmentation Network
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  norm_layer : object
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`).
  aux : bool
      Auxiliary loss.
  
  
  Reference:
  
      Rudra P K Poudel, et al. https://bmvc2019.org/wp-content/uploads/papers/0959-paper.pdf
      "Fast-SCNN: Fast Semantic Segmentation Network." *BMVC*, 2019
class_docstr_tok: ["Fast-SCNN",":","Fast","Semantic","Segmentation","Network","Parameters","--","--","--","--","--","nclass",":","int","Number","of","categories","for","the","training","dataset",".","norm_layer",":","object","Normalization","layer","used","in","backbone","network","(","default",":",":","class",":","`","mxnet.gluon.nn.BatchNorm","`",")",".","aux",":","bool","Auxiliary","loss",".","Reference",":","Rudra","P","K","Poudel",",","et","al",".","https",":","\/\/bmvc2019.org\/wp-content\/uploads\/papers\/0959-paper.pdf","``","Fast-SCNN",":","Fast","Semantic","Segmentation","Network",".","''","*","BMVC","*",",","2019"]
docstr: hybrid forward for Fast SCNN
docstr_tok: ["hybrid","forward","for","Fast","SCNN"]
code: def hybrid_forward(self, F, x):
      """hybrid forward for Fast SCNN"""
      higher_res_features = self.learning_to_downsample(x)
      x = self.global_feature_extractor(higher_res_features)
      x = self.feature_fusion(higher_res_features, x)
      x = self.classifier(x)
      x = F.contrib.BilinearResize2D(x, **self._up_kwargs)
  
      outputs = []
      outputs.append(x)
      if self.aux:
          auxout = self.auxlayer(higher_res_features)
          auxout = F.contrib.BilinearResize2D(auxout, **self._up_kwargs)
          outputs.append(auxout)
      return tuple(outputs)
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 2 for calling the FastSCNN obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: tuple