我想使用https://github.com/mmbejani/TikhonovRegularizationTerm。这个库包括正则化吉洪诺夫条款的实现，从2010年到现在（2020年）出版。
我试图在mnist数据集上用这个库来训练一个非常基本的前馈nn，并得到以下错误：

AttributeError: 'WeightDecay' object has no attribute 'backward'

optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)
time0 = time()
epochs = 15
for e in range(epochs):
    running_loss = 0
    for images, labels in trainloader:
        # Flatten MNIST images into a 784 long vector
        images = images.view(images.shape[0], -1)
    
        # Training pass
        optimizer.zero_grad()
        
        output = model(images)
        loss_function = nn.CrossEntropyLoss()
        loss_function_with_regularization = WeightDecay(model, loss_function)

        #This is where the model learns by backpropagating
        loss_function_with_regularization.backward()
        
        #And optimizes its weights here
        optimizer.step()
        
        running_loss += loss.item()
    else:
        print("Epoch {} - Training loss: {}".format(e, running_loss/len(trainloader)))
print("\nTraining Time (in minutes) =",(time()-time0)/60)`