class_docstr: Base Model for Semantic Segmentation
  
  Parameters
  ----------
  backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
  norm_layer : Block
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
      for Synchronized Cross-GPU BachNormalization).
class_docstr_tok: ["Base","Model","for","Semantic","Segmentation","Parameters","--","--","--","--","--","backbone",":","string","Pre-trained","dilated","backbone","network","type","(","default",":","'resnet50","'",";","'resnet50","'",",","'resnet101","'","or","'resnet152","'",")",".","norm_layer",":","Block","Normalization","layer","used","in","backbone","network","(","default",":",":","class",":","`","mxnet.gluon.nn.BatchNorm","`",";","for","Synchronized","Cross-GPU","BachNormalization",")","."]
code: def demo(self, x):
      warnings.warn(
          "demo method will be deprecated in favor of predict."
      )
      return self.predict(x)
  
imports: import math
  import warnings
  import numpy
  import mxnet
  from mxnet.ndarray import NDArray
  from mxnet.gluon.nn import HybridBlock
  from ..utils.parallel import parallel_apply
  from .resnetv1b import resnet18_v1b, resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
  from .resnest import resnest50, resnest101, resnest200, resnest269
  from ..utils.parallel import tuple_map
label_desc: Use image node_id 4 for calling the SegBaseModel obj's underlying member method code with example usage: obj.demo(x) and returns: self