code: def forward(self, x):
      size = x.size()[2:]
      features = self.base_forward(x)
  
      x = list(self.head(*features))
      x[0] = F.interpolate(
          x[0], size, mode="bilinear", align_corners=True
      )
      if self.aux and self.training:
          auxout = self.auxlayer(features[2])
          auxout = F.interpolate(
              auxout, size, mode="bilinear", align_corners=True
          )
          x.append(auxout)
          return tuple(x)
      else:
          return x[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 EncNet obj's underlying member method code with example usage: obj.forward(x) and returns: tuple, x