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 base_forward(self, x):
      # Entry flow
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
  
      x = self.conv2(x)
      x = self.bn2(x)
      x = self.relu(x)
  
      x = self.block1(x)
      # add relu here
      x = self.relu(x)
      low_level_feat = x
  
      x = self.block2(x)
      x = self.block3(x)
  
      # Middle flow
      x = self.midflow(x)
      mid_level_feat = x
  
      # Exit flow
      x = self.block20(x)
      x = self.relu(x)
      x = self.conv3(x)
      x = self.bn3(x)
      x = self.relu(x)
  
      x = self.conv4(x)
      x = self.bn4(x)
      x = self.relu(x)
  
      x = self.conv5(x)
      x = self.bn5(x)
      x = self.relu(x)
      return low_level_feat, mid_level_feat, x
  
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 2 for calling the DeepLabV3Plus obj's underlying member method code with example usage: obj.base_forward(x) and returns: low_level_feat,  mid_level_feat,  x