根据提示，补充代码，构建出满足打印输出要求结当实例化结果打印时结构如下，即为通过测试： LeNet( (conv): Sequential( (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1)) (1): Sigmoid() (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1)) (4): Sigmoid() (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (fc): Sequential( (0): Linear(in_features=256, out_features=120, bias=True) (1): Sigmoid() (2): Linear(in_features=120, out_features=84, bias=True) (3): Sigmoid() (4): Linear(in_features=84, out_features=10, bias=True) ) )构的 LeNet 模型。

可以使用以下代码构建 LeNet 模型：

import torch.nn as nn

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5),
            nn.Sigmoid(),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5),
            nn.Sigmoid(),
            nn.MaxPool2d(kernel_size=2)
        )
        
        self.fc = nn.Sequential(
            nn.Linear(in_features=256, out_features=120),
            nn.Sigmoid(),
            nn.Linear(in_features=120, out_features=84),
            nn.Sigmoid(),
            nn.Linear(in_features=84, out_features=10)
        )

    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

lenet = LeNet()
print(lenet)