text: import torch
  from torch import nn, Tensor
  import torch.nn.functional as F
  from typing import Optional, Tuple, List, Callable, Any
  
  model_urls = {
      # GoogLeNet ported from TensorFlow
      'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
  }
  
  
  class GoogLeNet(nn.Module):
  
      def __init__(self, num_classes: int = 1000, aux_logits: bool = True, init_weights=False, ) -> None:
          super(GoogLeNet, self).__init__()
  
          self.aux_logits = aux_logits
  
          self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
          self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
          self.conv2 = BasicConv2d(64, 64, kernel_size=1)
          self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
          self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
  
          self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
          self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
          self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
  
          self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
          self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
          self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
          self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
          self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
          self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
          self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
          self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
  
          if aux_logits:
              self.aux1 = InceptionAux(512, num_classes)
              self.aux2 = InceptionAux(528, num_classes)
          else:
              self.aux1 = None  # type: ignore[assignment]
              self.aux2 = None  # type: ignore[assignment]
  
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
          self.dropout = nn.Dropout(0.2)
          self.fc = nn.Linear(1024, num_classes)
  
          if init_weights:
              self._initialize_weights()
  
      def forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
          # N x 3 x 224 x 224
          x = self.conv1(x)
          # N x 64 x 112 x 112
          x = self.maxpool1(x)
          # N x 64 x 56 x 56
          x = self.conv2(x)
          # N x 64 x 56 x 56
          x = self.conv3(x)
          # N x 192 x 56 x 56
          x = self.maxpool2(x)
  
          # N x 192 x 28 x 28
          x = self.inception3a(x)
          # N x 256 x 28 x 28
          x = self.inception3b(x)
          # N x 480 x 28 x 28
          x = self.maxpool3(x)
          # N x 480 x 14 x 14
          x = self.inception4a(x)
          # N x 512 x 14 x 14
          if self.training and self.aux_logits:
              aux1 = self.aux1(x)
  
          x = self.inception4b(x)
          # N x 512 x 14 x 14
          x = self.inception4c(x)
          # N x 512 x 14 x 14
          x = self.inception4d(x)
          # N x 528 x 14 x 14
          if self.training and self.aux_logits:
              aux2 = self.aux2(x)
  
          x = self.inception4e(x)
          # N x 832 x 14 x 14
          x = self.maxpool4(x)
          # N x 832 x 7 x 7
          x = self.inception5a(x)
          # N x 832 x 7 x 7
          x = self.inception5b(x)
          # N x 1024 x 7 x 7
  
          x = self.avgpool(x)
          # N x 1024 x 1 x 1
          x = torch.flatten(x, 1)
          # N x 1024
          x = self.dropout(x)
          x = self.fc(x)
          # N x 1000 (num_classes)
          if self.training and self.aux_logits:
              return x, aux2, aux1
  
          return x
  
      def _initialize_weights(self) -> None:
          for m in self.modules():
              if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                  torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=0.01, a=-2, b=2)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
  
  class BasicConv2d(nn.Module):
  
      def __init__(self, in_channels: int, out_channels: int, **kwargs: Any) -> None:
          super(BasicConv2d, self).__init__()
          self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
          self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
  
      def forward(self, x: Tensor) -> Tensor:
          x = self.conv(x)
          x = self.bn(x)
          return F.relu(x, inplace=True)
  
  
  class Inception(nn.Module):
  
      def __init__(self, in_channels: int, ch1x1: int, ch3x3red: int, ch3x3: int,
                   ch5x5red: int, ch5x5: int, pool_proj: int) -> None:
          super(Inception, self).__init__()
  
          self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
  
          self.branch2 = nn.Sequential(
              BasicConv2d(in_channels, ch3x3red, kernel_size=1),
              BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)
          )
  
          self.branch3 = nn.Sequential(
              BasicConv2d(in_channels, ch5x5red, kernel_size=1),
              # Here, kernel_size=3 instead of kernel_size=5 is a known bug.
              # Please see https://github.com/pytorch/vision/issues/906 for details.
              # BasicConv2d(ch5x5red, ch5x5, kernel_size=3, padding=1)
              BasicConv2d(ch5x5red, ch5x5, kernel_size=5, padding=2)
          )
  
          self.branch4 = nn.Sequential(
              nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
              BasicConv2d(in_channels, pool_proj, kernel_size=1)
          )
  
      def forward(self, x: Tensor) -> Tensor:
          branch1 = self.branch1(x)
          branch2 = self.branch2(x)
          branch3 = self.branch3(x)
          branch4 = self.branch4(x)
  
          outputs = [branch1, branch2, branch3, branch4]
          return torch.cat(outputs, 1)
  
  
  class InceptionAux(nn.Module):
  
      def __init__(self, in_channels: int, num_classes: int) -> None:
          super(InceptionAux, self).__init__()
          self.conv = BasicConv2d(in_channels, 128, kernel_size=1)
  
          self.fc1 = nn.Linear(2048, 1024)
          self.fc2 = nn.Linear(1024, num_classes)
  
      def forward(self, x: Tensor) -> Tensor:
          # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
          x = F.adaptive_avg_pool2d(x, (4, 4))
          # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
          x = self.conv(x)
          # N x 128 x 4 x 4
          x = torch.flatten(x, start_dim=1)
          # N x 2048
          x = F.relu(self.fc1(x), inplace=True)
          # N x 1024
          x = F.dropout(x, 0.7, training=self.training)
          # N x 1024
          x = self.fc2(x)
          # N x 1000 (num_classes)
  
          return x
  
  
  if __name__ == '__main__':
      net = GoogLeNet()
      print(net)
  