code: def get_dunet(
      num_classes=1, backbone="resnet50", pretrained=True, **kwargs
  ):
      r"""Decoders Matter for Semantic Segmentation
  
      Parameters
      ----------
      num_classes : int (default: 1) - number of classes
      backbone : str - type of backbone to use (one of `{resnet50, resnet101, resnet152}`)
      pretrained : bool (default: True) - whether to load pretrained backbone network, that was trained on ImageNet.
      """
      model = DUNet(
          num_classes=num_classes,
          backbone=backbone,
          pretrained=pretrained,
          **kwargs,
      )
      return model
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
  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_dunet(num_classes, backbone, pretrained) and returns: model