code: def _make_layer(
      self, block, planes, blocks, stride=1, norm_layer=nn.BatchNorm2d
  ):
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              conv1x1(self.inplanes, planes * block.expansion, stride),
              norm_layer(planes * block.expansion),
          )
  
      layers = []
      layers.append(block(self.inplanes, planes, stride, downsample))
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(block(self.inplanes, planes))
  
      return nn.Sequential(*layers)
  
imports: import torch
  import torch.nn
  import torch.utils.model_zoo
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride, norm_layer) and returns: nn