code: def test_mut_operator_stats_update():
      """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
  
imports: from tpot import TPOTClassifier
  from tpot.gp_deap import initialize_stats_dict
  from deap import creator
  import random
label_desc: Use image node_id 3 for calling a global function with example usage: test_mut_operator_stats_update() without return types