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: Returns multiple sets of trial (hyper-)parameters,
  as iterable of serializable objects.
docstr_tok: ["Returns","multiple","sets","of","trial","(","hyper-",")","parameters",",","as","iterable","of","serializable","objects","."]
code: def generate_multiple_parameters(self, parameter_id_list, **kwargs):
      """Returns multiple sets of trial (hyper-)parameters,
      as iterable of serializable objects.
      """
      result = []
      self.send_trial_callback = kwargs["st_callback"]
      for parameter_id in parameter_id_list:
          had_exception = False
          try:
              self.logger.debug("generating param for %s", parameter_id)
              res = self.generate_parameters(parameter_id, **kwargs)
          except nni.NoMoreTrialError:
              had_exception = True
          if not had_exception:
              result.append(res)
      return result
  
imports: import math
  import random
  import logging
  import copy
  import nni
  from nni.tuner import Tuner
label_desc: Use image node_id 3 for calling the G_BFS obj's underlying member method code with example usage: obj.generate_multiple_parameters(parameter_id_list) and returns: result