code: def get_denseaspp(
      num_classes=1, backbone="densenet169", pretrained=True, **kwargs
  ):
      r"""DenseASPP
  
      Parameters
      ----------
      dataset : str, default citys
          The dataset that model pretrained on. (pascal_voc, ade20k)
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      root : str, default '~/.torch/models'
          Location for keeping the model parameters.
      pretrained_base : bool or str, default True
          This will load pretrained backbone network, that was trained on ImageNet.
  
      """
  
      return DenseASPP(
          num_classes=num_classes,
          pretrained=pretrained,
          backbone=backbone,
          **kwargs,
      )
  
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 for calling a global function with example usage: get_denseaspp(num_classes, backbone, pretrained) and returns: DenseASPP