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 __init__(
      self,
      nclass,
      aux,
      backbone="resnet50",
      height=None,
      width=None,
      base_size=520,
      crop_size=480,
      pretrained_base=True,
      **kwargs,
  ):
      super(SegBaseModel, self).__init__()
      self.aux = aux
      self.nclass = nclass
      with self.name_scope():
          pretrained = get_backbone(
              backbone,
              pretrained=pretrained_base,
              dilated=True,
              **kwargs,
          )
          self.conv1 = pretrained.conv1
          self.bn1 = pretrained.bn1
          self.relu = pretrained.relu
          self.maxpool = pretrained.maxpool
          self.layer1 = pretrained.layer1
          self.layer2 = pretrained.layer2
          self.layer3 = pretrained.layer3
          self.layer4 = pretrained.layer4
      height = height if height is not None else crop_size
      width = width if width is not None else crop_size
      self._up_kwargs = {"height": height, "width": width}
      self.base_size = base_size
      self.crop_size = crop_size
  
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 1 to create a new SegBaseModel object from inherited base classes: HybridBlock with example: obj = SegBaseModel(nclass, aux, backbone, height, width, base_size, crop_size, pretrained_base)