//use tensor_rs::tensor::Tensor;
//use auto_diff::var::{Module, Var, bcewithlogitsloss};

//fn alexnet(x: Var) {
//      def __init__(self, num_classes=1000):
//        super(AlexNet, self).__init__()
//        self.features = nn.Sequential(
//            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
//            nn.ReLU(inplace=True),
//            nn.MaxPool2d(kernel_size=3, stride=2),
//            nn.Conv2d(64, 192, kernel_size=5, padding=2),
//            nn.ReLU(inplace=True),
//            nn.MaxPool2d(kernel_size=3, stride=2),
//            nn.Conv2d(192, 384, kernel_size=3, padding=1),
//            nn.ReLU(inplace=True),
//            nn.Conv2d(384, 256, kernel_size=3, padding=1),
//            nn.ReLU(inplace=True),
//            nn.Conv2d(256, 256, kernel_size=3, padding=1),
//            nn.ReLU(inplace=True),
//            nn.MaxPool2d(kernel_size=3, stride=2),
//        )
//        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
//        self.classifier = nn.Sequential(
//            nn.Dropout(),
//            nn.Linear(256 * 6 * 6, 4096),
//            nn.ReLU(inplace=True),
//            nn.Dropout(),
//            nn.Linear(4096, 4096),
//            nn.ReLU(inplace=True),
//            nn.Linear(4096, num_classes),
//        )
//
//    def forward(self, x):
//        x = self.features(x)
//        x = self.avgpool(x)
//        x = torch.flatten(x, 1)
//        x = self.classifier(x)
//        return x
//}

fn main() {
}