code: def _make_layers(self, cfg):
      layers = []
      in_channels = 3
      for x in cfg:
          if x == "M":
              layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
          else:
              layers += [
                  nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                  nn.BatchNorm2d(x),
                  nn.ReLU(inplace=True),
              ]
              in_channels = x
      layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
      return nn.Sequential(*layers)
  
file_path: nni/examples/trials/cifar10_pytorch/models/vgg.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the VGG obj's underlying member method code with example usage: obj._make_layers(cfg) and returns: nn