class_docstr: Pyramid Scene Parsing Network
  
  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:
      Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu.
      "Dual Attention Network for Scene Segmentation." *CVPR*, 2019
code: def __init__(
      self,
      num_classes,
      pretrained=True,
      backbone="resnet101",
      aux=False,
      **kwargs,
  ):
      super(DANet, self).__init__(
          num_classes,
          pretrained=pretrained,
          aux=aux,
          backbone=backbone,
          **kwargs,
      )
      self.head = _DAHead(2048, num_classes, aux, **kwargs)
  
      self.__setattr__("exclusive", ["head"])
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
label_desc: Use image node_id 1 to create a new DANet object from inherited base classes: SegBaseModel with example: obj = DANet(num_classes, pretrained, backbone, aux)