text: import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  import math
  
  
  __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',
  }
  
  
  class VGG(nn.Module):
  
      def __init__(self, features, num_classes=7):
          super(VGG, self).__init__()
          self.features = features
          self.classifier = nn.Linear(52224, num_classes)
          self._initialize_weights()
  
      def forward(self, x):
          x = self.features(x)
          x = x.view(x.size(0), -1)
          x = self.classifier(x)
          return x
  
      def _initialize_weights(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  n = m.weight.size(1)
                  m.weight.data.normal_(0, 0.01)
                  m.bias.data.zero_()
  
  
  def make_layers(cfg, batch_norm=False):
      layers = []
      in_channels = 1
      for v in cfg:
          if v == 'M':
              layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
          else:
              conv2d = nn.Conv2d(in_channels, v, kernel_size=2, 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)
  
  
  cfg = {
      '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 vgg11(**kwargs):
      """VGG 11-layer model (configuration "A")
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = VGG(make_layers(cfg['A']), **kwargs)
      return model
  
  
  def vgg11_bn(**kwargs):
      """VGG 11-layer model (configuration "A") with batch normalization"""
      model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
      return model
  
  
  def vgg13(**kwargs):
      """VGG 13-layer model (configuration "B")
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = VGG(make_layers(cfg['B']), **kwargs)
      return model
  
  
  def vgg13_bn(**kwargs):
      """VGG 13-layer model (configuration "B") with batch normalization"""
      model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)
      return model
  
  
  def vgg16(**kwargs):
      """VGG 16-layer model (configuration "D")
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = VGG(make_layers(cfg['D']), **kwargs)
      return model
  
  
  def vgg16_bn(**kwargs):
      """VGG 16-layer model (configuration "D") with batch normalization"""
      model = VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
      return model
  
  
  def vgg19(**kwargs):
      """VGG 19-layer model (configuration "E")
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = VGG(make_layers(cfg['E']), **kwargs)
      return model
  
  
  def vgg19_bn(**kwargs):
      """VGG 19-layer model (configuration 'E') with batch normalization"""
      model = VGG(make_layers(cfg['E'], batch_norm=True), **kwargs)
      return model