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 _generate_initial_population(self):
      while len(self.initial_population) < self.population_size:
          self.initial_population.append(self._random_model())
      logger.info("init population done.")
  
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 9 for calling the RegularizedEvolutionTuner obj's underlying member method code with example usage: obj._generate_initial_population() without return types