def METHOD_NAME(cfg: DictConfig) -> None:
    if cfg.cpc1_track == "open":
        track = "_indep"
    elif cfg.cpc1_track == "closed":
        track = ""
    else:
        logger.error("cpc1_track has to be closed or open")
    # encoder representation evaluation
    prediction_dev, label_dev = read_data(
        Path(cfg.path.exp_folder) / "dev_enc_similarity.json",
        Path(cfg.path.cpc1_train_data) / f"metadata/CPC1.{'train'+track}.json",
    )
    prediction_test, label_test = read_data(
        Path(cfg.path.exp_folder) / "test_enc_similarity.json",
        Path(f"../test_listener_responses/CPC1.{'test'+track}.json"),
    )
    logger.info("Apply logistic fitting.")
    model = Model()
    model.fit(prediction_dev, label_dev)
    fit_pred = model.predict(prediction_test)
    enc_scores = compute_scores(fit_pred, label_test)
    # decoder representation evaluation
    prediction_dev, label_dev = read_data(
        Path(cfg.path.exp_folder) / "dev_dec_similarity.json",
        Path(cfg.path.cpc1_train_data) / f"metadata/CPC1.{'train'+track}.json",
    )
    prediction_test, label_test = read_data(
        Path(cfg.path.exp_folder) / "test_dec_similarity.json",
        Path(f"../test_listener_responses/CPC1.{'test'+track}.json"),
    )
    logger.info("Apply logistic fitting.")
    model = Model()
    model.fit(prediction_dev, label_dev)
    fit_pred = model.predict(prediction_test)
    dec_scores = compute_scores(fit_pred, label_test)
    results_file = Path(cfg.path.exp_folder) / "results.json"
    with results_file.open("w", encoding="utf-8") as fp:
        json.dump({"enc_results": enc_scores, "dec_results": dec_scores}, fp)