code: def test_trainer_save_load(self):
      trainer = Trainer(max_epochs=1)
      pl_model = Trainer.compile(self.model, self.loss, self.optimizer)
      trainer.save(pl_model, "saved_model")
      assert len(os.listdir("saved_model")) > 0
  
      # save original parameters
      original_state_dict = copy.deepcopy(pl_model.state_dict())
  
      # update paramters
      trainer.fit(pl_model, self.train_loader)
  
      loaded_pl_model = trainer.load("saved_model", pl_model)
      loaded_state_dict = loaded_pl_model.state_dict()
      # check if parameters are updated
      for k in original_state_dict.keys():
          assert (original_state_dict[k] == loaded_state_dict[k]).all()
      shutil.rmtree("saved_model")
  
imports: import copy
  import os
  import shutil
  from unittest import TestCase
  import pytest
  import torch
  from pytorch_lightning import LightningModule
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from test.pytorch.utils._train_torch_lightning import train_with_linear_top_layer
  from torch import nn
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch.vision.models import vision
label_desc: Use image node_id 3 for calling the TestTrainer obj's underlying member method code with example usage: obj.test_trainer_save_load() without return types