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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _mm(n_items, data, initial_params, alpha, max_iter, tol, mm_fun):
 """ Iteratively refine MM estimates until convergence. Raises ------ RuntimeError If the algorithm does not converge after `max_iter` iterations. """

    if initial_params is None:
        params = np.zeros(n_items)
    else:
        params = initial_params
    converged = NormOfDifferenceTest(tol=tol, order=1)
    for _ in range(max_iter):
        nums, denoms = mm_fun(n_items, data, params)
        params = log_transform((nums + alpha) / (denoms + alpha))
        if converged(params):
            return params
    raise RuntimeError("Did not converge after {} iterations".format(max_iter))