How to use the tabpy.models.utils.setup_utils function in tabpy

if component <= 0 or component > len(dataDict):
        print("ERROR: Component specified must be >= 0 and " "<= number of arguments")
        raise ValueError

    df = pd.DataFrame(data=dataDict, dtype=float)
    scale = StandardScaler()
    scaledData = scale.fit_transform(df)

    pca = sklearnPCA()
    pcaComponents = pca.fit_transform(scaledData)

    return pcaComponents[:, component - 1].tolist()


if __name__ == "__main__":
    setup_utils.deploy_model("PCA", PCA, "Returns the specified principal component")

ANOVA is a statistical hypothesis test that is used to compare
    two or more group means for equality.For more information on
    the function and how to use it please refer to tabpy-tools.md
    """

    cols = [_arg1, _arg2] + list(_argN)
    for col in cols:
        if not isinstance(col[0], (int, float)):
            print("values must be numeric")
            raise ValueError
    _, p_value = stats.f_oneway(_arg1, _arg2, *_argN)
    return p_value


if __name__ == "__main__":
    setup_utils.deploy_model("anova", anova, "Returns the p-value form an ANOVA test")

scores = []
    if library == "nltk":
        sid = SentimentIntensityAnalyzer()
        for text in _arg1:
            sentimentResults = sid.polarity_scores(text)
            score = sentimentResults["compound"]
            scores.append(score)
    elif library == "textblob":
        for text in _arg1:
            currScore = TextBlob(text)
            scores.append(currScore.sentiment.polarity)
    return scores


if __name__ == "__main__":
    setup_utils.deploy_model(
        "Sentiment Analysis",
        SentimentAnalysis,
        "Returns a sentiment score between -1 and 1 for " "a given string",
    )

def main():
    install_dependencies(["sklearn", "pandas", "numpy", "textblob", "nltk", "scipy"])
    print("==================================================================")
    # Determine if we run python or python3
    if platform.system() == "Windows":
        py = "python"
    else:
        py = "python3"

    if len(sys.argv) > 1:
        config_file_path = sys.argv[1]
    else:
        config_file_path = setup_utils.get_default_config_file_path()
    print(f"Using config file at {config_file_path}")
    port, auth_on, prefix = setup_utils.parse_config(config_file_path)
    if auth_on:
        auth_args = setup_utils.get_creds()
    else:
        auth_args = []

    directory = str(Path(__file__).resolve().parent / "scripts")
    # Deploy each model in the scripts directory
    for filename in os.listdir(directory):
        subprocess.run([py, f"{directory}/{filename}", config_file_path] + auth_args)

install_dependencies(["sklearn", "pandas", "numpy", "textblob", "nltk", "scipy"])
    print("==================================================================")
    # Determine if we run python or python3
    if platform.system() == "Windows":
        py = "python"
    else:
        py = "python3"

    if len(sys.argv) > 1:
        config_file_path = sys.argv[1]
    else:
        config_file_path = setup_utils.get_default_config_file_path()
    print(f"Using config file at {config_file_path}")
    port, auth_on, prefix = setup_utils.parse_config(config_file_path)
    if auth_on:
        auth_args = setup_utils.get_creds()
    else:
        auth_args = []

    directory = str(Path(__file__).resolve().parent / "scripts")
    # Deploy each model in the scripts directory
    for filename in os.listdir(directory):
        subprocess.run([py, f"{directory}/{filename}", config_file_path] + auth_args)