code: def forward(self, x):
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.conv2(x)
      x = self.bn2(x)
      x = self.relu(x)
  
      x = self.layer1(x)
      x = [
          trans(x) for trans in self.transition1
      ]  # Since now, x is a list
  
      x = self.stage2(x)
      x = [
          self.transition2[0](x[0]),
          self.transition2[1](x[1]),
          self.transition2[2](x[-1]),
      ]  # New branch derives from the "upper" branch only
  
      x = self.stage3(x)
      x = [
          self.transition3[0](x[0]),
          self.transition3[1](x[1]),
          self.transition3[2](x[2]),
          self.transition3[3](x[-1]),
      ]  # New branch derives from the "upper" branch only
  
      x = self.stage4(x)
  
      x = self.final_layer(x[0])
  
      return x
  
file_path: deep-learning-for-image-processing/pytorch_keypoint/HRNet/model/hrnet.py
imports: import torch.nn
label_desc: Use image node_id 2 for calling the HighResolutionNet obj's underlying member method code with example usage: obj.forward(x) and returns: x