code: def _make_stage(
      self, layer_config, num_inchannels, multi_scale_output=True
  ):
      num_modules = layer_config["NUM_MODULES"]
      num_branches = layer_config["NUM_BRANCHES"]
      num_blocks = layer_config["NUM_BLOCKS"]
      num_channels = layer_config["NUM_CHANNELS"]
      block = blocks_dict[layer_config["BLOCK"]]
      fuse_method = layer_config["FUSE_METHOD"]
  
      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,
              )
          )
          num_inchannels = modules[-1].get_num_inchannels()
  
      return nn.Sequential(*modules), num_inchannels
  
imports: import numpy
  import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from torchvision.models.utils import load_state_dict_from_url
label_desc: Use image node_id 4 for calling the HighResolutionNet obj's underlying member method code with example usage: obj._make_stage(layer_config, num_inchannels, multi_scale_output) and returns: nn,  num_inchannels