code: def _make_layer(
      self, in_planes, out_planes, num_blocks, dense_depth, stride
  ):
      strides = [stride] + [1] * (num_blocks - 1)
      layers = []
      for i, stride in enumerate(strides):
          layers.append(
              Bottleneck(
                  self.last_planes,
                  in_planes,
                  out_planes,
                  dense_depth,
                  stride,
                  i == 0,
              )
          )
          self.last_planes = out_planes + (i + 2) * dense_depth
      return nn.Sequential(*layers)
  
file_path: nni/examples/trials/cifar10_pytorch/models/dpn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the DPN obj's underlying member method code with example usage: obj._make_layer(in_planes, out_planes, num_blocks, dense_depth, stride) and returns: nn