class_docstr: Decoders Matter for Semantic Segmentation
  
  Reference:
      Zhi Tian, Tong He, Chunhua Shen, and Youliang Yan.
      "Decoders Matter for Semantic Segmentation:
      Data-Dependent Decoding Enables Flexible Feature Aggregation." CVPR, 2019
code: def forward(self, x):
      c1, c2, c3, c4 = self.base_forward(x)
      outputs = []
      x = self.head(c2, c3, c4)
      x = self.dupsample(x)
      outputs.append(x)
  
      if self.aux and self.training:
          auxout = self.auxlayer(c3)
          auxout = self.aux_dupsample(auxout)
          outputs.append(auxout)
          return tuple(outputs)
      else:
          return outputs[0]
  
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 2 for calling the DUNet obj's underlying member method code with example usage: obj.forward(x) and returns: tuple, outputs