def METHOD_NAME(classifier, train_data, optimizer):
    classifier.METHOD_NAME()
    x, label = train_data
    x, label = x.to(device), label.to(device)
    for step in range(100):
        # forward
        optimizer.zero_grad()
        pred_logits = classifier(x)
        # loss and backprop
        loss = criterion(pred_logits, label)
        loss.backward()
        optimizer.step()