code: def forward(self, x):
      # 每个分支通过对应的block
      x = [branch(xi) for branch, xi in zip(self.branches, x)]
  
      # 接着融合不同尺寸信息
      x_fused = []
      for i in range(len(self.fuse_layers)):
          x_fused.append(
              self.relu(
                  sum(
                      [
                          self.fuse_layers[i][j](x[j])
                          for j in range(len(self.branches))
                      ]
                  )
              )
          )
  
      return x_fused
  
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 StageModule obj's underlying member method code with example usage: obj.forward(x) and returns: x_fused