class_docstr: DeepLabV3Plus
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  backbone : string
      Pre-trained dilated backbone network type (default:'xception').
  norm_layer : object
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
      for Synchronized Cross-GPU BachNormalization).
  aux : bool
      Auxiliary loss.
  
  
  Reference:
  
      Chen, Liang-Chieh, et al. "Encoder-Decoder with Atrous Separable Convolution for Semantic
      Image Segmentation."
class_docstr_tok: ["DeepLabV3Plus","Parameters","--","--","--","--","--","nclass",":","int","Number","of","categories","for","the","training","dataset",".","backbone",":","string","Pre-trained","dilated","backbone","network","type","(","default",":","'xception","'",")",".","norm_layer",":","object","Normalization","layer","used","in","backbone","network","(","default",":",":","class",":","`","mxnet.gluon.nn.BatchNorm","`",";","for","Synchronized","Cross-GPU","BachNormalization",")",".","aux",":","bool","Auxiliary","loss",".","Reference",":","Chen",",","Liang-Chieh",",","et","al",".","``","Encoder-Decoder","with","Atrous","Separable","Convolution","for","Semantic","Image","Segmentation",".","''"]
code: def demo(self, x):
      h, w = x.shape[2:]
      self._up_kwargs["height"] = h
      self._up_kwargs["width"] = w
      self.head.aspp.concurent[-1]._up_kwargs["height"] = h // 8
      self.head.aspp.concurent[-1]._up_kwargs["width"] = w // 8
      pred = self.forward(x)
      if self.aux:
          pred = pred[0]
      return pred
  
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from mxnet import gluon
  from .fcn import _FCNHead
  from .xception import get_xcetption
label_desc: Use image node_id 4 for calling the DeepLabV3Plus obj's underlying member method code with example usage: obj.demo(x) and returns: pred