code: def _make_stage(
      self,
      block,
      num_inchannels,
      num_modules,
      num_branches,
      num_blocks,
      num_channels,
      fuse_method="SUM",
      multi_scale_output=True,
  ):
      modules = []
      for i in range(num_modules):
          # multi_scale_output is only used last module
          if not multi_scale_output and i == num_modules - 1:
              reset_multi_scale_output = False
          else:
              reset_multi_scale_output = True
          modules.append(
              HighResolutionModule(
                  num_branches,
                  block,
                  num_blocks,
                  num_inchannels,
                  num_channels,
                  fuse_method,
                  reset_multi_scale_output,
                  norm_layer=self.norm_layer,
                  align_corners=self.align_corners,
              )
          )
          num_inchannels = modules[-1].get_num_inchannels()
  
      return nn.Sequential(*modules), num_inchannels
  
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 4 for calling the HighResolutionNet obj's underlying member method code with example usage: obj._make_stage(block, num_inchannels, num_modules, num_branches, num_blocks, num_channels, fuse_method, multi_scale_output) and returns: nn,  num_inchannels