code: def forward(self, x):
      input_size = (x.size()[2], x.size()[3])
  
      features = self.backbone(x)
      features[-1] = self.PPN(features[-1])
      x = self.head(self.FPN(features))
  
      x = F.interpolate(x, size=input_size, mode="bilinear")
      return x
  
imports: import torch
  import logging
  import torch.nn
  import torch.nn.functional
  from torchvision import models
  from itertools import chain
  import numpy
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling the UperNet obj's underlying member method code with example usage: obj.forward(x) and returns: x