def METHOD_NAME(save_path):
    dataset = PetFinderDataset()
    hyperparameters = {
        "env.num_workers": 0,
        "env.num_workers_evaluation": 0,
        "model.names": ["timm_image", "hf_text", "fusion_mlp"],
        "model.hf_text.checkpoint_name": "nlpaueb/legal-bert-small-uncased",
        "model.timm_image.checkpoint_name": "swin_tiny_patch4_window7_224",
    }
    abs_path = os.path.abspath(os.path.expanduser(save_path))
    if os.path.exists(abs_path):
        shutil.rmtree(abs_path)
    os.makedirs(abs_path, exist_ok=True)
    dummy_file_path = os.path.join(abs_path, "dummy.txt")
    with open(dummy_file_path, "w") as f:
        f.write("dummy")
    predictor = MultiModalPredictor(path=save_path)
    with pytest.raises(ValueError):
        predictor.fit(train_data=dataset.train_df, hyperparameters=hyperparameters)
    predictor = MultiModalPredictor(
        label=dataset.label_columns[0],
        problem_type=dataset.problem_type,
        eval_metric=dataset.metric,
    )
    with pytest.raises(ValueError):
        predictor.fit(train_data=dataset.train_df, hyperparameters=hyperparameters, save_path=save_path)
    shutil.rmtree(abs_path)