code: def make_features(cfg: list):
      layers = []
      in_channels = 3
      for v in cfg:
          if v == "M":
              layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
          else:
              conv2d = nn.Conv2d(
                  in_channels, v, kernel_size=3, padding=1
              )
              layers += [conv2d, nn.ReLU(True)]
              in_channels = v
      return nn.Sequential(*layers)
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/backbone/vgg_model.py
imports: import torch.nn
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: make_features(cfg) and returns: nn