class_docstr: G-BFS Tuner
  Based on paper Compiler-Level Matrix Multiplication Optimization for Deep Learning
  
  Parameters
  ----------
  optimize_mode: str, 'maximize' or 'minimize'
  num_samples: int,
      The random selection parameter rho
class_docstr_tok: ["G-BFS","Tuner","Based","on","paper","Compiler-Level","Matrix","Multiplication","Optimization","for","Deep","Learning","Parameters","--","--","--","--","--","optimize_mode",":","str",",","'maximize","'","or","'minimize'","num_samples",":","int",",","The","random","selection","parameter","rho"]
docstr: Update the self.bounds and self.types by the search_space.json file.
  
  Override of the abstract method in :class:`~nni.tuner.Tuner`.
docstr_tok: ["Update","the","self.bounds","and","self.types","by","the","search_space.json","file",".","Override","of","the","abstract","method","in",":","class",":","`","~nni.tuner.Tuner","`","."]
code: def update_search_space(self, search_space):
      """Update the self.bounds and self.types by the search_space.json file.
  
      Override of the abstract method in :class:`~nni.tuner.Tuner`.
      """
      if not isinstance(search_space, dict):
          self.logger.info("The format of search space is not a dict.")
          raise RuntimeError(
              "The format of search space is not a dict."
          )
  
      self.population = Population(
          self.opt_mode, search_space, self.num_samples
      )
  
      if not self.serve_list:
          self.serve_list = self.population.generate()
  
imports: import math
  import random
  import logging
  import copy
  import nni
  from nni.tuner import Tuner
label_desc: Use image node_id 2 for calling the G_BFS obj's underlying member method code with example usage: obj.update_search_space(search_space) without return types