class_docstr: DeepLabWV3Plus
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  backbone : string
      Pre-trained dilated backbone network type (default:'wideresnet').
  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.", https://arxiv.org/abs/1802.02611, ECCV 2018
class_docstr_tok: ["DeepLabWV3Plus","Parameters","--","--","--","--","--","nclass",":","int","Number","of","categories","for","the","training","dataset",".","backbone",":","string","Pre-trained","dilated","backbone","network","type","(","default",":","'wideresnet","'",")",".","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",".","``",",","https",":","\/\/arxiv.org\/abs\/1802.02611",",","ECCV","2018"]
code: def __init__(
      self,
      nclass,
      backbone="wideresnet",
      aux=False,
      ctx=cpu(),
      pretrained_base=True,
      height=None,
      width=None,
      base_size=520,
      crop_size=480,
      dilated=True,
      **kwargs,
  ):
      super(DeepLabWV3Plus, self).__init__()
  
      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
      # print('self.crop_size', self.crop_size)
      kwargs.pop("root", None)
  
      with self.name_scope():
          pretrained = wider_resnet38_a2(classes=1000, dilation=True)
          pretrained.initialize(ctx=ctx)
          self.mod1 = pretrained.mod1
          self.mod2 = pretrained.mod2
          self.mod3 = pretrained.mod3
          self.mod4 = pretrained.mod4
          self.mod5 = pretrained.mod5
          self.mod6 = pretrained.mod6
          self.mod7 = pretrained.mod7
          self.pool2 = pretrained.pool2
          self.pool3 = pretrained.pool3
          del pretrained
          self.head = _DeepLabHead(
              nclass, height=height // 2, width=width // 2, **kwargs
          )
          self.head.initialize(ctx=ctx)
  
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from .wideresnet import wider_resnet38_a2
label_desc: Use image node_id 1 to create a new DeepLabWV3Plus object from inherited base classes: HybridBlock with example: obj = DeepLabWV3Plus(nclass, backbone, aux, ctx, pretrained_base, height, width, base_size, crop_size, dilated)