code: def forward(self, x):
      x_size = x.size()
      pool3 = self.features3(x)
      pool4 = self.features4(pool3)
      pool5 = self.features5(pool4)
  
      score_fr = self.score_fr(pool5)
      upscore2 = self.upscore2(score_fr)
  
      score_pool4 = self.score_pool4(0.01 * pool4)
      upscore_pool4 = self.upscore_pool4(
          score_pool4[
              :,
              :,
              5 : (5 + upscore2.size()[2]),
              5 : (5 + upscore2.size()[3]),
          ]
          + upscore2
      )
  
      score_pool3 = self.score_pool3(0.0001 * pool3)
      upscore8 = self.upscore8(
          score_pool3[
              :,
              :,
              9 : (9 + upscore_pool4.size()[2]),
              9 : (9 + upscore_pool4.size()[3]),
          ]
          + upscore_pool4
      )
      return upscore8[
          :, :, 31 : (31 + x_size[2]), 31 : (31 + x_size[3])
      ].contiguous()
  
file_path: pywick/pywick/models/segmentation/fcn8s.py
imports: import torch
  from torch import nn
  from torchvision import models
  from .fcn_utils import get_upsampling_weight
  from .config import vgg16_path, vgg16_caffe_path
label_desc: Use image node_id 2 for calling the FCN8s obj's underlying member method code with example usage: obj.forward(x) and returns: upscore8