我希望你写一个基于MINIST数据集的神经网络，使用pytorch，实现手写数字分类。我希望有完整的代码结构，并输出测试结果。不要解释，给出代码

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.relu(x)
        x = self.conv2(x)
        x = torch.relu(x)
        x = torch.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = torch.log_softmax(x, dim=1)
        return output

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = torch.nn.functional.nll_loss(output, target)
        loss.backward()
        optimizer.step()
    print('Train Epoch: {} \tLoss: {:.6f}'.format(epoch, loss.item()))

def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += torch.nn.functional.nll_loss(output, target, reduction='sum').item() # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))

if __name__ == '__main__':
    torch.manual_seed(1)
    device = torch.device("cpu")

    kwargs = {'num_workers':