class_docstr: RegularizedEvolutionTuner is tuner using Evolution NAS Tuner.
  See ``Regularized Evolution for Image Classifier Architecture Search`` for details.
  
  Parameters
  ---
  optimize_mode: str
      whether to maximize metric or not. default: 'maximize'
  population_size: int
      the maximum number of kept models
  sample_size: int
      the number of models chosen from population each time when evolution
code: def _mutate_model(self, model):
      new_individual = copy.deepcopy(model.parameters)
      mutate_key = random.choice(list(new_individual.keys()))
      self._mutate(mutate_key, new_individual)
      return new_individual
  
imports: import copy
  import logging
  import random
  from collections import deque
  from schema import Schema, Optional
  import nni
  from nni.tuner import Tuner
  from nni import ClassArgsValidator
  from nni.utils import OptimizeMode, extract_scalar_reward
label_desc: Use image node_id 8 for calling the RegularizedEvolutionTuner obj's underlying member method code with example usage: obj._mutate_model(model) and returns: new_individual