code: def forward(self, x):
      pool4 = self.pool4(x)
      pool5 = self.pool5(pool4)
  
      outputs = []
      score_fr = self.head(pool5)
  
      score_pool4 = self.score_pool4(pool4)
  
      upscore2 = F.interpolate(
          score_fr,
          score_pool4.size()[2:],
          mode="bilinear",
          align_corners=True,
      )
      fuse_pool4 = upscore2 + score_pool4
  
      out = F.interpolate(
          fuse_pool4, x.size()[2:], mode="bilinear", align_corners=True
      )
      outputs.append(out)
  
      if self.aux and self.training:
          auxout = self.auxlayer(pool5)
          auxout = F.interpolate(
              auxout, x.size()[2:], mode="bilinear", align_corners=True
          )
          outputs.append(auxout)
          return tuple(outputs)
      else:
          return outputs[0]
  
file_path: pywick/pywick/models/segmentation/da_basenets/fcn.py
imports: import torch.nn
  import torch.nn.functional
  from .vgg import vgg16
label_desc: Use image node_id 2 for calling the FCN16s obj's underlying member method code with example usage: obj.forward(x) and returns: tuple, outputs