def METHOD_NAME(**kwargs):
    np = util.get_module("numpy", required="Logging plots requires numpy")
    pd = util.get_module("pandas", required="Logging dataframes requires pandas")
    scipy = util.get_module("scipy", required="Logging scipy matrices requires scipy")
    base = util.get_module(
        "sklearn.base",
        "roc requires the scikit base submodule, install with `pip install scikit-learn`",
    )
    test_passed = True
    for k, v in kwargs.items():
        # check for incorrect types
        if (
            (k == "X")
            or (k == "X_test")
            or (k == "y")
            or (k == "y_test")
            or (k == "y_true")
            or (k == "y_probas")
            or (k == "x_labels")
            or (k == "y_labels")
            or (k == "matrix_values")
        ):
            # FIXME: do this individually
            if not isinstance(
                v,
                (
                    Sequence,
                    Iterable,
                    np.ndarray,
                    np.generic,
                    pd.DataFrame,
                    pd.Series,
                    list,
                ),
            ):
                wandb.termerror("%s is not an array. Please try again." % (k))
                test_passed = False
        # check for classifier types
        if k == "model":
            if (not base.is_classifier(v)) and (not base.is_regressor(v)):
                wandb.termerror(
                    "%s is not a classifier or regressor. Please try again." % (k)
                )
                test_passed = False
        elif k == "clf" or k == "binary_clf":
            if not (base.is_classifier(v)):
                wandb.termerror("%s is not a classifier. Please try again." % (k))
                test_passed = False
        elif k == "regressor":
            if not base.is_regressor(v):
                wandb.termerror("%s is not a regressor. Please try again." % (k))
                test_passed = False
        elif k == "clusterer":
            if not (getattr(v, "_estimator_type", None) == "clusterer"):
                wandb.termerror("%s is not a clusterer. Please try again." % (k))
                test_passed = False
    return test_passed