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: evaluating network with inputs and targets
docstr_tok: ["evaluating","network","with","inputs","and","targets"]
code: def evaluate(self, x):
      """evaluating network with inputs and targets"""
      return self.forward(x)[0]
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 5 for calling the FastSCNN obj's underlying member method code with example usage: obj.evaluate(x) and returns: self