def METHOD_NAME():
    try:
        import sklearn
        import joblib
        if sklearn.__version__ < "0.20":
            gs.fatal("Package python3-scikit-learn 0.20 or newer is not installed")
    except ImportError:
        gs.fatal("Package python3-scikit-learn 0.20 or newer is not installed")
    # parser options
    group = options["group"]
    output = options["output"]
    model_load = options["load_model"]
    probability = flags["p"]
    prob_only = flags["z"]
    chunksize = int(options["chunksize"])
    # remove @ from output in case overwriting result
    if "@" in output:
        output = output.split("@")[0]
    # check probabilities=True if prob_only=True
    if prob_only is True and probability is False:
        gs.fatal("Need to set probabilities=True if prob_only=True")
    # reload fitted model and training data
    gs.message("Loading fitted model and training data ...")
    estimator, y, class_labels = joblib.load(model_load)
    # define RasterStack
    stack = RasterStack(group=group)
    # perform raster prediction
    region = Region()
    row_incr = math.ceil(chunksize / region.cols)
    # do not read by increments if increment > n_rows
    if row_incr >= region.rows:
        row_incr = None
    # prediction
    if prob_only is False:
        gs.message("Predicting classification/regression raster...")
        stack.predict(
            estimator=estimator,
            output=output,
            height=row_incr,
            overwrite=gs.overwrite(),
        )
    if probability is True:
        gs.message("Predicting class probabilities...")
        stack.predict_proba(
            estimator=estimator,
            output=output,
            class_labels=np.unique(y),
            overwrite=gs.overwrite(),
            height=row_incr,
        )
    # assign categories for classification map
    if class_labels and prob_only is False:
        rules = []
        for val, lab in class_labels.items():
            rules.append("|".join([str(val), str(lab)]))
        rules = "\n".join(rules)
        rules_file = string_to_rules(rules)
        r.category(map=output, rules=rules_file, separator="pipe")