用pytorch实现mnist手写数字识别，出现“raise NotImplementedError”请问是哪里的接口没有实现？该怎么该才是正确的呢？

问题遇到的现象和发生背景

用pytorch实现mnist手写数字识别

问题相关代码，请勿粘贴截图

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets,transforms
import torchvision
from torch.autograd import Variable
from torch.utils.data import DataLoader
train_dataset=datasets.MNIST(root='./num/',
                             train=True,
                             transform=transforms.ToTensor(),
                             download=True)
test_dataset=datasets.MNIST(root='./num/',
                             train=False,
                             transform=transforms.ToTensor(),
                             download=False)
batch_size=64
train_loader=torch.utils.data.DataLoader(dataset=train_dataset,
                                         batch_size=batch_size,
                                         shuffle=True)
test_loader=torch.utils.data.DataLoader(dataset=test_dataset,
                                        batch_size=batch_size,
                                        shuffle=True)
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1=nn.Sequential(nn.Conv2d(1,6,3,1,2),nn.ReLU(),
                                 nn.MaxPool2d(2,2))
        self.conv2=nn.Sequential(nn.Conv2d(6,16,5),nn.ReLU(),
                                 nn.MaxPool2d(2,2))
        self.fc1=nn.Sequential(nn.Linear(16*5*5,120),
                               nn.BatchNorm1d(120),nn.ReLU())
        self.fc2=nn.Sequential(
            nn.Linear(120,84),
            nn.BatchNorm1d(84),
            nn.ReLU(),
            nn.Linear(84,10))

def forward(self,x):
    x=self.conv1(x)
    x=self.conv2(x)
    x=x.view(x.size()[0],-1)
    x=self.fc1(x)
    x=self.fc2(x)
    return x

device=torch.device('cuda'if torch.cuda.is_available() else 'cpu')
batch_size=64
LR=0.001
net=LeNet().to(device)
criterion=nn.CrossEntropyLoss()
optimizer=optim.Adam(
    net.parameters(),
    lr=LR,)


epoch=1
if __name__ == '__main__':
    for epoch in range(epoch):
        sum_loss=0.0
        for i,data in enumerate(train_loader):
            inputs,labels=data
            inputs,labels=Variable(inputs).cuda(),Variable(labels).cuda()
            optimizer.zero_grad()
            outputs =net(inputs)
            loss=criterion(outputs,labels)
            loss.backward()
            optimizer.step()

            sum_loss+=loss.item()
            if i % 100 == 99:
                print('[%d,%d] loss:%.03f'%
                      (epoch + 1,i+1,sum_loss/100))
                sum_loss=0.0
correct=0
total=0
for data_test in test_loader:
    images,labels=data_test
    images,labels=Variable(images).cuda(),Variable(labels).cuda()
    output_test=net(images)
    _,predicted=torch.max(output_test,1)
    total+=labels.size(0)
    correct+=(predicted==labels).sum()
    print("correct1:",correct)
    print("test acc:{0}".format(correct.item()/
                                len(test_dataset)))

运行结果及报错内容

Traceback (most recent call last):
  File "C:\Users\12137\PycharmProjects\pythonProject2\main.py", line 65, in <module>
    outputs =net(inputs)
  File "D:\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 201, in _forward_unimplemented
    raise NotImplementedError
NotImplementedError

我的解答思路和尝试过的方法

我想要达到的结果

def forward没有和__init对齐
改后为

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1=nn.Sequential(nn.Conv2d(1,6,3,1,2),nn.ReLU(),
                                 nn.MaxPool2d(2,2))
        self.conv2=nn.Sequential(nn.Conv2d(6,16,5),nn.ReLU(),
                                 nn.MaxPool2d(2,2))
        self.fc1=nn.Sequential(nn.Linear(16*5*5,120),
                               nn.BatchNorm1d(120),nn.ReLU())
        self.fc2=nn.Sequential(
            nn.Linear(120,84),
            nn.BatchNorm1d(84),
            nn.ReLU(),
            nn.Linear(84,10))

    def forward(self,x):
        x=self.conv1(x)
        x=self.conv2(x)
        x=x.view(x.size()[0],-1)
        x=self.fc1(x)
        x=self.fc2(x)
        return x

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