code: def __init__(self, requires_grad=False, pretrained=True):
      super(vgg16, self).__init__()
      vgg_pretrained_features = models.vgg16(
          pretrained=pretrained
      ).features
      self.slice1 = torch.nn.Sequential()
      self.slice2 = torch.nn.Sequential()
      self.slice3 = torch.nn.Sequential()
      self.slice4 = torch.nn.Sequential()
      self.slice5 = torch.nn.Sequential()
      self.N_slices = 5
      for x in range(4):
          self.slice1.add_module(str(x), vgg_pretrained_features[x])
      for x in range(4, 9):
          self.slice2.add_module(str(x), vgg_pretrained_features[x])
      for x in range(9, 16):
          self.slice3.add_module(str(x), vgg_pretrained_features[x])
      for x in range(16, 23):
          self.slice4.add_module(str(x), vgg_pretrained_features[x])
      for x in range(23, 30):
          self.slice5.add_module(str(x), vgg_pretrained_features[x])
      if not requires_grad:
          for param in self.parameters():
              param.requires_grad = False
  
imports: from collections import namedtuple
  import torch
  import torch.nn
  from torchvision import models
  from ..util import get_ckpt_path
label_desc: Use image node_id 1 to create a new vgg16 object from inherited base classes: torch.nn with example: obj = vgg16(requires_grad, pretrained)