class_docstr: A sequential domain reduction transformer based on the work by Stander, N. and Craig, K:
  "On the robustness of a simple domain reduction scheme for simulation‐based optimization"
class_docstr_tok: ["A","sequential","domain","reduction","transformer","based","on","the","work","by","Stander",",","N.","and","Craig",",","K",":","''","On","the","robustness","of","a","simple","domain","reduction","scheme","for","simulation\u2010based","optimization","''"]
code: def _create_bounds(self, parameters: dict, bounds: np.array) -> dict:
      return {param: bounds[i, :] for i, param in enumerate(parameters)}
  
file_path: BayesianOptimization/bayes_opt/domain_reduction.py
imports: from typing import Optional, Union, List
  import numpy
  from .target_space import TargetSpace
  from warnings import warn
label_desc: Use image node_id 6 for calling the SequentialDomainReductionTransformer obj's underlying member method code with example usage: obj._create_bounds(parameters, bounds) and returns: unknown