How to use the ngboost.manifold.manifold function in ngboost

    metric_fn = lambda p: manifold(CRPScore, Normal)(np.array(p)[:, np.newaxis]).metric()
    grad_fn = lambda p: manifold(CRPScore, Normal)(np.array(p)[:, np.newaxis]).d_score(rvs).mean(axis=0)

    fisher_fn = lambda p: manifold(LogScore, Normal)(np.array(p)[:, np.newaxis]).metric()
    grad_fn = lambda p: manifold(LogScore, Normal)(np.array(p)[:, np.newaxis]).d_score(rvs).mean(axis=0)

    nll_fn = lambda p: manifold(LogScore, Normal)(np.array(p)[:, np.newaxis]).score(rvs).mean()
    fisher_fn = lambda p: manifold(LogScore, Normal)(np.array(p)[:, np.newaxis]).metric()

def __init__(self, Dist=Normal, Score=LogScore,
                 Base=default_tree_learner, gradient='natural',
                 n_estimators=500, learning_rate=0.01, minibatch_frac=1.0,
                 verbose=True, verbose_eval=100, tol=1e-4,
                 random_state=None):
        self.Dist = Dist
        self.Score = Score
        self.Base = Base
        self.Manifold = manifold(Score, Dist)
        self.gradient = gradient
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.minibatch_frac = minibatch_frac
        self.verbose = verbose
        self.verbose_eval = verbose_eval
        self.init_params = None
        self.base_models = []
        self.scalings = []
        self.tol = tol
        self.random_state = check_random_state(random_state)
        self.best_val_loss_itr = None

    grad_fn = lambda p: manifold(LogScore, Normal)(np.array(p)[:, np.newaxis]).d_score(rvs).mean(axis=0)