def METHOD_NAME(input_model=None, total_epochs=2, lr=0.001):
    net.load_state_dict(input_model.params)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9)
    # (optional) use GPU to speed things up
    net.to(DEVICE)
    # (optional) calculate total steps
    steps = 2 * len(trainloader)
    for epoch in range(total_epochs):  # loop over the dataset multiple times
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            # (optional) use GPU to speed things up
            inputs, labels = data[0].to(DEVICE), data[1].to(DEVICE)
            # zero the parameter gradients
            optimizer.zero_grad()
            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print(f"[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}")
                running_loss = 0.0
    print("Finished Training")
    torch.save(net.state_dict(), PATH)
    # (4) construct trained FL model
    output_model = flare.FLModel(params=net.cpu().state_dict(), meta={"NUM_STEPS_CURRENT_ROUND": steps})
    return output_model