def METHOD_NAME(model):
    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")
    scikit_utils = util.get_module(
        "sklearn.utils",
        required="roc requires the scikit utils submodule, install with `pip install scikit-learn`",
    )
    scikit_exceptions = util.get_module(
        "sklearn.exceptions",
        "roc requires the scikit preprocessing submodule, install with `pip install scikit-learn`",
    )
    try:
        model.predict(np.zeros((7, 3)))
    except scikit_exceptions.NotFittedError:
        wandb.termerror("Please fit the model before passing it in.")
        return False
    except AttributeError:
        # Some clustering models (LDA, PCA, Agglomerative) don't implement ``predict``
        try:
            scikit_utils.validation.check_is_fitted(
                model,
                [
                    "coef_",
                    "estimator_",
                    "labels_",
                    "n_clusters_",
                    "children_",
                    "components_",
                    "n_components_",
                    "n_iter_",
                    "n_batch_iter_",
                    "explained_variance_",
                    "singular_values_",
                    "mean_",
                ],
                all_or_any=any,
            )
            return True
        except scikit_exceptions.NotFittedError:
            wandb.termerror("Please fit the model before passing it in.")
            return False
    except Exception:
        # Assume it's fitted, since ``NotFittedError`` wasn't raised
        return True