code: def forward(self, x):
      # suppose input = x , if x 512
      f0 = self.layer0(x)  # 256
      f1 = self.layer1(f0)  # 128
      f2 = self.layer2(f1)  # 64
      f3 = self.layer3(f2)  # 32
      f4 = self.layer4(f3)  # 16
  
      gcfm1 = self.brm1(self.gcm1(f4))  # 16
      gcfm2 = self.brm2(self.gcm2(f3))  # 32
      gcfm3 = self.brm3(self.gcm3(f2))  # 64
      gcfm4 = self.brm4(self.gcm4(f1))  # 128
  
      fs1 = self.brm5(self.deconv1(gcfm1) + gcfm2)  # 32
      fs2 = self.brm6(self.deconv2(fs1) + gcfm3)  # 64
      fs3 = self.brm7(self.deconv3(fs2) + gcfm4)  # 128
      fs4 = self.brm8(self.deconv4(fs3))  # 256
      out = self.brm9(self.deconv5(fs4))
  
      return out
  
file_path: pywick/pywick/models/segmentation/testnets/lg_kernel_exfuse/large_kernel.py
imports: import torch
  from torch import nn
  from .deeplab_resnet import ModelBuilder
label_desc: Use image node_id 2 for calling the GCN obj's underlying member method code with example usage: obj.forward(x) and returns: out