code: def forward(self, x):
      size = x.size()[2:]
      features = self.pretrained.features(x)
      if self.dilate_scale > 8:
          features = F.interpolate(
              features,
              scale_factor=2,
              mode="bilinear",
              align_corners=True,
          )
      outputs = []
      x = self.head(features)
      x = F.interpolate(x, size, mode="bilinear", align_corners=True)
      outputs.append(x)
  
      if self.aux and self.training:
          auxout = self.auxlayer(features)
          auxout = F.interpolate(
              auxout, size, mode="bilinear", align_corners=True
          )
          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.densenet import *
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 2 for calling the DenseASPP obj's underlying member method code with example usage: obj.forward(x) and returns: tuple, outputs