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: Update search space.
  Search_space contains the information that user pre-defined.
  
  Parameters
  ----------
  search_space : dict
docstr_tok: ["Update","search","space",".","Search_space","contains","the","information","that","user","pre-defined",".","Parameters","--","--","--","--","--","search_space",":","dict"]
code: def update_search_space(self, search_space):
      """
      Update search space.
      Search_space contains the information that user pre-defined.
  
      Parameters
      ----------
      search_space : dict
      """
      logger.info("update search space %s", search_space)
      assert self.search_space is None
      self.search_space = search_space
  
      for _, val in search_space.items():
          if (
              val["_type"] != "layer_choice"
              and val["_type"] != "input_choice"
          ):
              raise ValueError(
                  "Unsupported search space type: %s" % (val["_type"])
              )
  
      self._generate_initial_population()
  
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 4 for calling the RegularizedEvolutionTuner obj's underlying member method code with example usage: obj.update_search_space(search_space) without return types