code: def get_hyperparameter_configurations(self, num, r, config_generator):
          
          global _KEY
          assert self.i == 0
          hyperparameter_configs = dict()
          for _ in range(num):
              params_id = create_bracket_parameter_id(self.s, self.i)
              params = config_generator.get_config(r)
              params[_KEY] = r
              hyperparameter_configs[params_id] = params
          self._record_hyper_configs(hyperparameter_configs)
          return [[key, value] for key, value in hyperparameter_configs.items()]
docs: generate num hyperparameter configurations from search space using Bayesian optimization
  
          Parameters
          ----------
          num: int
              the number of hyperparameter configurations
  
          Returns
          -------
          list
              a list of hyperparameter configurations. Format: [[key1, value1], [key2, value2], ...]