class_docstr: DeepLabV3
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
  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. "Rethinking atrous convolution for semantic image segmentation."
      arXiv preprint arXiv:1706.05587 (2017).
code: def __init__(
      self,
      nclass,
      backbone="resnet50",
      aux=True,
      ctx=cpu(),
      pretrained_base=True,
      height=None,
      width=None,
      base_size=520,
      crop_size=480,
      **kwargs,
  ):
      super(DeepLabV3, self).__init__(
          nclass,
          aux,
          backbone,
          ctx=ctx,
          base_size=base_size,
          crop_size=crop_size,
          pretrained_base=pretrained_base,
          **kwargs,
      )
      kwargs.pop("root", None)
      height = height if height is not None else crop_size
      width = width if width is not None else crop_size
      with self.name_scope():
          self.head = _DeepLabHead(
              nclass, height=height // 8, width=width // 8, **kwargs
          )
          self.head.initialize(ctx=ctx)
          self.head.collect_params().setattr("lr_mult", 10)
          if self.aux:
              self.auxlayer = _FCNHead(1024, nclass, **kwargs)
              self.auxlayer.initialize(ctx=ctx)
              self.auxlayer.collect_params().setattr("lr_mult", 10)
      self._up_kwargs = {"height": height, "width": width}
  
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from mxnet import gluon
  from .segbase import SegBaseModel
  from .fcn import _FCNHead
label_desc: Use image node_id 1 to create a new DeepLabV3 object from inherited base classes: SegBaseModel with example: obj = DeepLabV3(nclass, backbone, aux, ctx, pretrained_base, height, width, base_size, crop_size)