code: def meta_opt(opt_para):
      scores = []
  
      for i in range(33):
  
          def ackley_function(para):
              x = para["x"]
              y = para["y"]
              loss1 = -20 * np.exp(
                  -0.2 * np.sqrt(0.5 * (x * x + y * y))
              )
              loss2 = -np.exp(
                  0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))
              )
              loss3 = np.exp(1)
              loss4 = 20
  
              loss = loss1 + loss2 + loss3 + loss4
  
              return -loss
  
          dim_size = np.arange(-6, 6, 0.01)
  
          search_space = {
              "x": dim_size,
              "y": dim_size,
          }
  
          opt = RandomRestartHillClimbingOptimizer(
              search_space,
              random_state=i,
              epsilon=opt_para["epsilon"],
              n_neighbours=opt_para["n_neighbours"],
              n_iter_restart=opt_para["n_iter_restart"],
          )
          opt.search(
              ackley_function,
              n_iter=100,
              verbosity=False,
          )
  
          scores.append(opt.best_score)
  
      return np.array(scores).sum()
  
imports: import numpy
  from hyperactive import Hyperactive
  from hyperactive.optimizers import BayesianOptimizer
  from gradient_free_optimizers import RandomRestartHillClimbingOptimizer
label_desc: Use image node_id 1 for calling a global function with example usage: meta_opt(opt_para) and returns: np, unknown