add runner.max_retries

This PR (mainly) introduces runner.max_retries and runner.raise_if_max_retries.

With this the following function would be "learned":

import adaptive
adaptive.notebook_extension()

def f(x, offset=0):
    from random import random
    a = 0.01
    if random() < 0.9:
        raise Exception('Oops, this failed.')
    return x + a**2 / (a**2 + (x - offset)**2)

learner = adaptive.Learner1D(f, bounds=(-1, 1))
runner = adaptive.BlockingRunner(learner, goal=lambda l: l.loss() < 0.05,
                         max_retries=20, log=True, raise_if_max_retries=False)

I also introduce BlockingRunner.overhead and the corresponding timing functions and put the shared code of BlockingRunner and AsyncRunner in BaseLearner methods.