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 __init__(
      self,
      optimize_mode="maximize",
      population_size=100,
      sample_size=25,
  ):
      super(RegularizedEvolutionTuner, self).__init__()
      self.optimize_mode = OptimizeMode(optimize_mode)
      self.population_size = population_size
      self.sample_size = sample_size
      self.initial_population = deque()
      self.population = deque()
      self.history = {}
      self.search_space = None
      self._from_initial = {}
  
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 1 to create a new RegularizedEvolutionTuner object from inherited base classes: Tuner with example: obj = RegularizedEvolutionTuner(optimize_mode, population_size, sample_size)