def METHOD_NAME(
    trainer_options,
    model: LightningModule,
    data: LightningDataModule = None,
    version=None,
    with_hpc: bool = True,
    min_acc: float = 0.25,
    min_change_ratio: float = 0.03,
):
    save_dir = trainer_options["default_root_dir"]
    # logger file to get meta
    logger = get_default_logger(save_dir, version=version)
    trainer_options.update(logger=logger)
    trainer = Trainer(**trainer_options)
    with torch.no_grad():
        initial_values = torch.cat([x.view(-1) for x in model.parameters()])
    trainer.fit(model, datamodule=data)
    with torch.no_grad():
        post_train_values = torch.cat([x.view(-1) for x in model.parameters()])
    # Check that the model has changed post-training
    change_ratio = torch.norm(initial_values - post_train_values) / torch.norm(initial_values)
    assert change_ratio >= min_change_ratio, (
        f"The change in the model's parameter norm is {change_ratio:.1f}"
        f" relative to the initial norm, but expected a change by >={min_change_ratio}"
    )
    if trainer.world_size != trainer.num_devices:
        # we're in multinode. unless the filesystem is shared, only the main node will have access to the checkpoint
        # since we cannot know this, the code below needs to be skipped
        return
    # test model loading
    _ = load_model_from_checkpoint(trainer.checkpoint_callback.best_model_path, type(model))
    # test new model accuracy
    test_loaders = model.test_dataloader() if not data else data.test_dataloader()
    if not isinstance(test_loaders, list):
        test_loaders = [test_loaders]
    if not isinstance(model, BoringModel):
        for dataloader in test_loaders:
            run_model_prediction(model, dataloader, min_acc=min_acc)
    if with_hpc:
        # test HPC saving
        # save logger to make sure we get all the metrics
        if logger:
            logger.finalize("finished")
        hpc_save_path = trainer._checkpoint_connector.hpc_save_path(save_dir)
        trainer.save_checkpoint(hpc_save_path)
        # test HPC loading
        checkpoint_path = trainer._checkpoint_connector._CheckpointConnector__get_max_ckpt_path_from_folder(save_dir)
        trainer._checkpoint_connector.restore(checkpoint_path)