code: def forward(self, x):
      enc1 = self.enc1(x)  ## 240x320x64 --> No Relu
      enc2 = self.enc2(
          self._do_downsample(F.relu(enc1))
      )  ## 120x160x128 --> No relu
      enc3 = self.enc3(
          self._do_downsample(F.relu(enc2))
      )  ## 60x80x256 -->
      enc4 = self.enc4(
          self._do_downsample(F.relu(enc3))
      )  ## 30x40x512 --> conv4
      middle = self.deconvbn1024_512(
          self.middle(self._do_downsample(F.relu(enc4)))
      )  ## 30x40x512 --> no relu
  
      dec1 = self.deconvbn512_256(
          self.dec1(F.relu(middle + enc4))
      )  ## 60x80x256
      dec2 = self.deconvbn256_128(
          self.dec2(F.relu(dec1 + enc3))
      )  ## 120x160x128
      dec3 = self.deconvbn128_64(
          self.dec3(F.relu(dec2 + enc2))
      )  ## 240x320x64
      dec4 = self.dec4(F.relu(dec3 + enc1))  ## 240x320x64
  
      output = self.final(dec4)
      return self.activation(output)
  
imports: import torch.nn
  import torch.nn.init
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 2 for calling the FusionNet obj's underlying member method code with example usage: obj.forward(x) and returns: self