如何将LBFGS优化器与pytorch ignite一起使用？

import torch
from torch import optim
from ignite.engine import Engine, Events
from ignite.contrib.handlers import ProgressBar

# 定义模型
model = ...

# 定义数据
data = ...

def train_step(engine, batch):
    model.train()
    optimizer.zero_grad()
    x, y = batch
    y_pred = model(x)
    loss = ...
    loss.backward()
    optimizer.step()
    return loss.item()

# 创建Ignite Engine
trainer = Engine(train_step)

# 创建LBFGS优化器
optimizer = optim.LBFGS(model.parameters(), lr=0.1)

# 添加进度条事件处理器
pbar = ProgressBar()
pbar.attach(trainer)

# 添加打印训练损失事件处理器
@trainer.on(Events.ITERATION_COMPLETED)
def log_training_loss(engine):
    print("Epoch[{}] Loss: {:.2f}".format(engine.state.epoch, engine.state.output))

# 添加停止训练条件事件处理器
@trainer.on(Events.EPOCH_COMPLETED)
def check_stop_condition(engine):
    if stop_condition:
        engine.terminate()

# 添加使用LBFGS优化器的事件处理器
@trainer.on(Events.EPOCH_STARTED)
def set_optimizer_params(engine):
    optimizer.set_params(lr=engine.state.epoch * 0.1)

trainer.run(data, max_epochs=10)