code: def __init__(
      self,
      features,
      class_num=1000,
      init_weights=False,
      weights_path=None,
  ):
      super(VGG, self).__init__()
      self.features = features
      self.classifier = nn.Sequential(
          nn.Linear(512 * 7 * 7, 4096),
          nn.ReLU(True),
          nn.Dropout(p=0.5),
          nn.Linear(4096, 4096),
          nn.ReLU(True),
          nn.Dropout(p=0.5),
          nn.Linear(4096, class_num),
      )
      if init_weights and weights_path is None:
          self._initialize_weights()
  
      if weights_path is not None:
          self.load_state_dict(torch.load(weights_path))
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/train_coco_dataset/backbone/vgg_model.py
imports: import torch.nn
  import torch
label_desc: Use image node_id 1 to create a new VGG object from inherited base classes: nn with example: obj = VGG(features, class_num, init_weights, weights_path)