code: def _make_layer(
      self, block, planes, blocks, stride=1, filter_size=1, dilate=False
  ):
      norm_layer = self._norm_layer
      downsample = None
      previous_dilation = self.dilation
      if dilate:
          self.dilation *= stride
          stride = 1
      if stride != 1 or self.inplanes != planes * block.expansion:
          # since this is just a conv1x1 layer (no nonlinearity),
          # conv1x1->blurpool is the same as blurpool->conv1x1; the latter is cheaper
          downsample = (
              [
                  BlurPool(
                      filt_size=filter_size,
                      stride=stride,
                      channels=self.inplanes,
                  ),
              ]
              if (stride != 1)
              else []
          )
          downsample += [
              conv1x1(self.inplanes, planes * block.expansion, 1),
              norm_layer(planes * block.expansion),
          ]
          downsample = nn.Sequential(*downsample)
  
      layers = []
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              downsample,
              self.groups,
              base_width=self.base_width,
              dilation=previous_dilation,
              norm_layer=norm_layer,
              filter_size=filter_size,
          )
      )
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  groups=self.groups,
                  base_width=self.base_width,
                  dilation=self.dilation,
                  norm_layer=norm_layer,
                  filter_size=filter_size,
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import torch.nn
  import torch.utils.model_zoo
  from antialiased_cnns import *
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, filter_size, dilate) and returns: nn