code: def _make_layer(
      self,
      block,
      out_channels,
      nblocks,
      stride=1,
      level=0.2,
      nmasks=None,
      use_act=False,
      filter_size=None,
      act=None,
      input_size=None,
  ):
      shortcut = None
      if stride != 1 or self.nfilters != out_channels * block.expansion:
          shortcut = nn.Sequential(
              nn.Conv2d(
                  self.nfilters,
                  out_channels * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(out_channels * block.expansion),
          )
      layers = []
      layers.append(
          block(
              self.nfilters,
              out_channels,
              stride,
              shortcut,
              level=level,
              nmasks=nmasks,
              use_act=use_act,
              filter_size=filter_size,
              act=act,
              unique_masks=self.unique_masks,
              noise_type=self.noise_type,
              train_masks=self.train_masks,
              input_size=input_size,
              pool_type=self.pool_type,
              mix_maps=self.mix_maps,
          )
      )
      self.nfilters = out_channels * block.expansion
      for i in range(1, nblocks):
          layers.append(
              block(
                  self.nfilters,
                  out_channels,
                  level=level,
                  nmasks=nmasks,
                  use_act=use_act,
                  train_masks=self.train_masks,
                  filter_size=filter_size,
                  act=act,
                  unique_masks=self.unique_masks,
                  noise_type=self.noise_type,
                  input_size=input_size // stride,
                  pool_type=self.pool_type,
                  mix_maps=self.mix_maps,
              )
          )
      return nn.Sequential(*layers)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the PerturbResNet obj's underlying member method code with example usage: obj._make_layer(block, out_channels, nblocks, stride, level, nmasks, use_act, filter_size, act, input_size) and returns: nn