code: def __init__(
      self,
      num_classes,
      pretrained=True,
      backbone="densenet161",
      aux=False,
      dilate_scale=8,
      **kwargs,
  ):
      super(DenseASPP, self).__init__()
      self.nclass = num_classes
      self.aux = aux
      self.dilate_scale = dilate_scale
      if backbone == "densenet121":
          self.pretrained = dilated_densenet121(
              dilate_scale, pretrained=pretrained, **kwargs
          )
      elif backbone == "densenet161":
          self.pretrained = dilated_densenet161(
              dilate_scale, pretrained=pretrained, **kwargs
          )
      elif backbone == "densenet169":
          self.pretrained = dilated_densenet169(
              dilate_scale, pretrained=pretrained, **kwargs
          )
      elif backbone == "densenet201":
          self.pretrained = dilated_densenet201(
              dilate_scale, pretrained=pretrained, **kwargs
          )
      else:
          raise RuntimeError("unknown backbone: {}".format(backbone))
      in_channels = self.pretrained.num_features
  
      self.head = _DenseASPPHead(in_channels, num_classes, **kwargs)
  
      if aux:
          self.auxlayer = _FCNHead(in_channels, num_classes, **kwargs)
  
      self.__setattr__(
          "exclusive", ["head", "auxlayer"] if aux else ["head"]
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.densenet import *
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 1 to create a new DenseASPP object from inherited base classes: nn with example: obj = DenseASPP(num_classes, pretrained, backbone, aux, dilate_scale)