code: def forward(self, x):
      # Assuming input of size 240x320
  
      x = self.layer0(x)  ## 120x160x64
  
      layer1 = self.layer1(x)  ## 60x80x256
      layer2 = self.layer2(layer1)  ## 30x40x512
      layer3 = self.layer3(layer2)  ## 15x 20x1024
      layer4 = self.layer4(layer3)  ## 7x10x2048
  
      enc1 = self.br256(self.gcn256(layer1))
      enc2 = self.br512(self.gcn512(layer2))
      enc3 = self.br1024(self.gcn1024(layer3))
      enc4 = self.br2048(self.gcn2048(layer4))  ## 8x10x1
  
      dec1 = self.br1(
          F.interpolate(enc4, size=enc3.size()[2:], mode="bilinear")
          + enc3
      )
      dec2 = self.br2(
          F.interpolate(dec1, enc2.size()[2:], mode="bilinear") + enc2
      )
      dec3 = self.br3(
          F.interpolate(dec2, enc1.size()[2:], mode="bilinear") + enc1
      )
      dec4 = self.br4(self.deconv1(dec3))
  
      score_map = self.br5(self.deconv2(dec4))
  
      return self.activation(score_map)
  
file_path: pywick/pywick/models/segmentation/resnet_gcn.py
imports: import torch.nn
  import torch.nn.init
  import torch.nn.functional
  import torchvision.models
  import numpy
label_desc: Use image node_id 2 for calling the ResnetGCN obj's underlying member method code with example usage: obj.forward(x) and returns: self