def METHOD_NAME():
    download_dir = "./"
    save_path = "petfinder_checkpoint"
    train_df, tune_df = shopee_dataset(download_dir=download_dir)
    if os.path.isdir(save_path):
        shutil.rmtree(save_path)
    predictor = MultiModalPredictor(label="label", path=save_path)
    hyperparameters = {
        "model.names": ["timm_image"],
        "model.timm_image.checkpoint_name": "ghostnet_100",
        "env.num_workers": 0,
        "env.num_workers_evaluation": 0,
        "optimization.top_k_average_method": "best",
        "optimization.val_check_interval": 1.0,
        "optimization.skip_final_val": True,
    }
    predictor.fit(
        train_data=train_df,
        tuning_data=tune_df,
        hyperparameters=hyperparameters,
        time_limit=5,
    )
    predictor_new = MultiModalPredictor.load(path=save_path)
    assert isinstance(predictor_new._model, TimmAutoModelForImagePrediction)