code: def _make_layer(self, block, inplanes, planes, blocks, stride=1):
      downsample = None
      if stride != 1 or inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2d(
                  inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              self.norm_layer(planes * block.expansion),
          )
  
      layers = []
      layers.append(
          block(
              inplanes,
              planes,
              stride,
              downsample,
              norm_layer=self.norm_layer,
          )
      )
      inplanes = planes * block.expansion
      for i in range(1, blocks):
          layers.append(
              block(inplanes, planes, norm_layer=self.norm_layer)
          )
  
      return nn.Sequential(*layers)
  
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 3 for calling the HighResolutionNet obj's underlying member method code with example usage: obj._make_layer(block, inplanes, planes, blocks, stride) and returns: nn