def METHOD_NAME():
    model_dump_path = "./test_fusion_models"
    dataset = ALL_DATASETS["petfinder"]
    predictor = MultiModalPredictor(
        label=dataset.label_columns[0], problem_type=dataset.problem_type, eval_metric=dataset.metric
    )
    hyperparameters = {
        "optimization.max_epochs": 1,
        "model.names": ["timm_image", "hf_text", "fusion_mlp"],
        "model.timm_image.checkpoint_name": "ghostnet_100",
        "model.hf_text.checkpoint_name": "nlpaueb/legal-bert-small-uncased",
    }
    predictor.fit(
        train_data=dataset.train_df,
        hyperparameters=hyperparameters,
        time_limit=5,
        seed=42,
    )
    predictor.dump_model(save_path=model_dump_path)
    hf_text_dir = f"{model_dump_path}/hf_text"
    timm_image_dir = f"{model_dump_path}/timm_image"
    assert os.path.exists(hf_text_dir) and (len(os.listdir(hf_text_dir)) > 2) == True
    assert os.path.exists(timm_image_dir) and (len(os.listdir(timm_image_dir)) == 2) == True