def METHOD_NAME(*args):
    df = pd.DataFrame([args], columns=X_train.columns)
    df = df.astype({col: "category" for col in categorical_columns})
    shap_values = explainer.shap_values(xgb.DMatrix(df, enable_categorical=True))
    scores_desc = list(zip(shap_values[0], X_train.columns))
    scores_desc = sorted(scores_desc)
    fig_m = plt.figure(tight_layout=True)
    plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
    plt.title("Feature Shap Values")
    plt.ylabel("Shap Value")
    plt.xlabel("Feature")
    plt.tight_layout()
    return fig_m