code: def compute_hrnet_feats(
      self, x, additional_features, return_list=False
  ):
      x = self.compute_pre_stage_features(x, additional_features)
      x = self.layer1(x)
  
      x_list = []
      for i in range(self.stage2_num_branches):
          if self.transition1[i] is not None:
              x_list.append(self.transition1[i](x))
          else:
              x_list.append(x)
      y_list = self.stage2(x_list)
  
      x_list = []
      for i in range(self.stage3_num_branches):
          if self.transition2[i] is not None:
              if i < self.stage2_num_branches:
                  x_list.append(self.transition2[i](y_list[i]))
              else:
                  x_list.append(self.transition2[i](y_list[-1]))
          else:
              x_list.append(y_list[i])
      y_list = self.stage3(x_list)
  
      x_list = []
      for i in range(self.stage4_num_branches):
          if self.transition3[i] is not None:
              if i < self.stage3_num_branches:
                  x_list.append(self.transition3[i](y_list[i]))
              else:
                  x_list.append(self.transition3[i](y_list[-1]))
          else:
              x_list.append(y_list[i])
      x = self.stage4(x_list)
  
      if return_list:
          return x
  
      # Upsampling
      x0_h, x0_w = x[0].size(2), x[0].size(3)
      x1 = F.interpolate(
          x[1],
          size=(x0_h, x0_w),
          mode="bilinear",
          align_corners=self.align_corners,
      )
      x2 = F.interpolate(
          x[2],
          size=(x0_h, x0_w),
          mode="bilinear",
          align_corners=self.align_corners,
      )
      x3 = F.interpolate(
          x[3],
          size=(x0_h, x0_w),
          mode="bilinear",
          align_corners=self.align_corners,
      )
  
      return torch.cat([x[0], x1, x2, x3], 1)
  
imports: import os
  import torch
  import torch._utils
  import numpy
  import torch.nn
  import torch.nn.functional
  from cftool.misc import print_info
  from .ocr import SpatialOCR_Module
  from .ocr import SpatialGather_Module
  from .resnetv1b import BottleneckV1b
  from .resnetv1b import BasicBlockV1b
label_desc: Use image node_id 6 for calling the HighResolutionNet obj's underlying member method code with example usage: obj.compute_hrnet_feats(x, additional_features, return_list) and returns: torch, x