(LearnerND) add advanced usage example about adaptive HOT 2 OPEN

bl = BalancingLearner(learners)
learner_a = bl.get("abc")

def isoline_loss_function(y_iso, sigma, priority=1):
    from adaptive.learner.learnerND import default_loss

    def gaussian(x, mu, sigma):
        return np.exp(-(x - mu) ** 2 / sigma ** 2 / 2)

    def loss(simplex, values, value_scale):
        distance = np.mean([abs(y_iso * value_scale - y) for y in values])
        return priority * gaussian(distance, 0, sigma) + default_loss(simplex, values, value_scale)

    return loss


level = 0.1
loss = isoline_loss_function(level, sigma=0.4, priority=0.5)