code: def forward(self, x):
      x_size = x.size()
      pool5 = self.features5(x)
      score_fr = self.score_fr(pool5)
      upscore = self.upscore(score_fr)
      return upscore[
          :, :, 19 : (19 + x_size[2]), 19 : (19 + x_size[3])
      ].contiguous()
  
file_path: pywick/pywick/models/segmentation/fcn32s.py
imports: import torch
  from torch import nn
  from torchvision import models
  from .fcn_utils import get_upsampling_weight
  from .config import vgg16_caffe_path
label_desc: Use image node_id 2 for calling the FCN32VGG obj's underlying member method code with example usage: obj.forward(x) and returns: upscore