text: import torch
  import torch.nn as nn
  from torch.hub import load_state_dict_from_url
  from typing import Union, List, Dict, Any, cast
  
  
  __all__ = [
      'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
      'vgg19_bn', 'vgg19',
  ]
  
  
  model_urls = {
      'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
      'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
      'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
      'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
      'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
      'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
      'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
      'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
  }
  
  
  class VGG(nn.Module):
  
      def __init__(
          self,
          features: nn.Module,
          num_classes: int = 1000,
          init_weights: bool = True
      ) -> None:
          super(VGG, self).__init__()
          self.features = features
          self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
          self.classifier = nn.Sequential(
              nn.Linear(512 * 7 * 7, 4096),
              nn.ReLU(True),
              nn.Dropout(),
              nn.Linear(4096, 4096),
              nn.ReLU(True),
              nn.Dropout(),
              nn.Linear(4096, num_classes),
          )
          if init_weights:
              self._initialize_weights()
  
      def forward(self, x: torch.Tensor) -> torch.Tensor:
          x = self.features(x)
          x = self.avgpool(x)
          x = torch.flatten(x, 1)
          x = self.classifier(x)
          return x
  
      def _initialize_weights(self) -> None:
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                  if m.bias is not None:
                      nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.Linear):
                  nn.init.normal_(m.weight, 0, 0.01)
                  nn.init.constant_(m.bias, 0)
  
  
  def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential:
      layers: List[nn.Module] = []
      in_channels = 3
      for v in cfg:
          if v == 'M':
              layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
          else:
              v = cast(int, v)
              conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
              if batch_norm:
                  layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
              else:
                  layers += [conv2d, nn.ReLU(inplace=True)]
              in_channels = v
      return nn.Sequential(*layers)
  
  
  cfgs: Dict[str, List[Union[str, int]]] = {
      'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
      'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
      'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
      'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
  }
  
  
  def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, **kwargs: Any) -> VGG:
      if pretrained:
          kwargs['init_weights'] = False
      model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
      if pretrained:
          state_dict = load_state_dict_from_url(model_urls[arch],
                                                progress=progress)
          model.load_state_dict(state_dict)
      return model
  
  
  def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
      r"""VGG 11-layer model (configuration "A") from
      `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs)
  
  
  
  def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
      r"""VGG 13-layer model (configuration "B")
      `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs)
  
  
  
  def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
      r"""VGG 16-layer model (configuration "D")
      `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs)
  
  
  
  def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
      r"""VGG 19-layer model (configuration "E")
      `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs)