text: import torch
  import tqt
  import tqt.function as nn
  
  
  class LeNet(nn.Module):
      def __init__(self):
          super().__init__()
          self.conv1 = nn.Conv2d(1, 6, 5, padding=2)
          self.mp1 = nn.MaxPool2d((2, 2))
          self.relu1 = nn.ReLU()
          self.conv2 = nn.Conv2d(6, 16, 5)
          self.mp2 = nn.MaxPool2d((2, 2))
          self.relu2 = nn.ReLU()
          self.conv3 = nn.Conv2d(16, 120, 5)
          self.relu3 = nn.ReLU()
          self.fc1 = nn.Linear(120, 84)
          self.relu4 = nn.ReLU()
          self.fc2 = nn.Linear(84, 10)
          self.proc = [
              'conv1', 'mp1', 'relu1', 'conv2', 'mp2', 'relu2', 'conv3', 'relu3',
              'fc1', 'relu4', 'fc2'
          ]
  
      def forward(self, x):
          for p in self.proc:
              x = getattr(self, p)(x)
              if p == 'conv3':
                  x = x.reshape(x.shape[0], -1)
          return x
  
  
  if __name__ == '__main__':
      x = torch.rand(3, 1, 28, 28)
      net = LeNet()
      tqt.utils.make_net_quant_or_not(net,
                                      'net',
                                      quant=True,
                                      exclude=[torch.nn.ReLU],
                                      show=True)
      tqt.threshold.init.init_network(net, net, 'net', show=True)
      print(net(x).shape)
  