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 trial_end(self, parameter_id, success, **kwargs):
      if not success:
          del self.history[parameter_id]
          if self._from_initial[parameter_id]:
              self.initial_population.append(self._random_model())
          del self._from_initial[parameter_id]
  
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 5 for calling the RegularizedEvolutionTuner obj's underlying member method code with example usage: obj.trial_end(parameter_id, success) without return types