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
docstr: Record the result from a trial
  
  Parameters
  ----------
  parameter_id : int
  parameters : dict
  value : dict/float
      if value is dict, it should have "default" key.
      value is final metrics of the trial.
docstr_tok: ["Record","the","result","from","a","trial","Parameters","--","--","--","--","--","parameter_id",":","int","parameters",":","dict","value",":","dict\/float","if","value","is","dict",",","it","should","have","``","default","''","key",".","value","is","final","metrics","of","the","trial","."]
code: def receive_trial_result(
      self, parameter_id, parameters, value, **kwargs
  ):
      """
      Record the result from a trial
  
      Parameters
      ----------
      parameter_id : int
      parameters : dict
      value : dict/float
          if value is dict, it should have "default" key.
          value is final metrics of the trial.
      """
      reward = extract_scalar_reward(value)
      if parameter_id not in self.history:
          raise RuntimeError("Received parameter_id not in total_data.")
      params = self.history[parameter_id]
  
      if self.optimize_mode == OptimizeMode.Minimize:
          reward = -reward
  
      self.population.append(
          FinishedIndividual(parameter_id, params, reward)
      )
      if len(self.population) > self.population_size:
          self.population.popleft()
  
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 3 for calling the RegularizedEvolutionTuner obj's underlying member method code with example usage: obj.receive_trial_result(parameter_id, parameters, value) without return types