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