def METHOD_NAME():
    """Asserts that self._random_mutation_operator updates stats as expected."""
    tpot_obj = TPOTClassifier()
    tpot_obj._fit_init()
    ind = creator.Individual.from_string(
        'KNeighborsClassifier('
        'BernoulliNB(input_matrix, BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=False),'
        'KNeighborsClassifier__n_neighbors=10, '
        'KNeighborsClassifier__p=1, '
        'KNeighborsClassifier__weights=uniform'
        ')',
        tpot_obj._pset
    )
    initialize_stats_dict(ind)
    ind.statistics["crossover_count"] = random.randint(0, 10)
    ind.statistics["mutation_count"] = random.randint(0, 10)
    # set as evaluated pipelines in tpot_obj.evaluated_individuals_
    tpot_obj.evaluated_individuals_[str(ind)] = tpot_obj._combine_individual_stats(2, 0.99, ind.statistics)
    for _ in range(10):
        offspring, = tpot_obj._random_mutation_operator(ind)
        assert offspring.statistics['crossover_count'] == ind.statistics['crossover_count']
        assert offspring.statistics['mutation_count'] == ind.statistics['mutation_count'] + 1
        assert offspring.statistics['predecessor'] == (str(ind),)
        ind = offspring