code: def test_trainer_save_checkpoint(self):
      # `save_checkpoint` may report an error when using ipex 1.9 and custom lr_scheduler
      trainer = Trainer(max_epochs=max_epochs, use_ipex=True)
      pl_model = Trainer.compile(
          self.model, self.loss, self.optimizer, self.scheduler_dict
      )
      trainer.fit(pl_model, self.train_loader)
  
imports: import os
  from unittest import TestCase
  import pytest
  import torch
  from torch.optim.lr_scheduler import OneCycleLR
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from test.pytorch.utils._train_ipex_callback import CheckIPEXFusedStepCallback
  from torch import nn
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_10, TORCH_VERSION_LESS_2_0
  from bigdl.nano.utils.common import _avx2_checker
label_desc: Use image node_id 1 for calling the PLTrainer obj's underlying member method code with example usage: obj.test_trainer_save_checkpoint() without return types