code: def run_eval(
      problem_config,
      f_inits,
      method_config,
      name="run",
      outpath=".",
      time_limit=-1,
      unittest=False,
  ):
      """
      This is a driver for running the optimization in the unittests.
      """
  
      # Methods configuration
      m_c = method_config
  
      # Initial values
      xs_init = f_inits[:]
      f_obj = problem_config["objective"]
      ys_init = f_obj(xs_init)
  
      # Create the Bayesian Optimization Object
      bo = GPyOpt.methods.BayesianOptimization(
          f=problem_config["objective"],
          domain=problem_config["domain"],
          constraints=problem_config["constraints"],
          cost_withGradients=problem_config["cost_withGradients"],
          model_type=m_c["model_type"],
          X=xs_init.copy(),
          Y=ys_init.copy(),
          initial_design_numdata=m_c["initial_design_numdata"],
          initial_design_type=m_c["initial_design_type"],
          acquisition_type=m_c["acquisition_type"],
          normalize_Y=m_c["normalize_Y"],
          exact_feval=m_c["exact_feval"],
          acquisition_optimizer_type=m_c["acquisition_optimizer_type"],
          model_update_interval=m_c["model_update_interval"],
          verbosity=m_c["verbosity"],
          evaluator_type=m_c["evaluator_type"],
          batch_size=m_c["batch_size"],
          num_cores=m_c["num_cores"],
          de_duplication=m_c.get("de_duplication", False),
      )
  
      if "context" not in problem_config.keys():
          problem_config["context"] = None
  
      # Run the optimization
      bo.run_optimization(
          max_iter=m_c["max_iter"],
          max_time=m_c["max_time"],
          eps=m_c["eps"],
          verbosity=m_c["verbosity"],
          context=problem_config["context"],
      )
  
      # Save value of X in a file
      results = bo.X
  
      # Used to run the original resuult used for testing
      if unittest == False:
          np.savetxt(os.path.join(outpath, name + ".txt"), results)
          print(
              "*********************************************************************************"
          )
          print(
              "This is not a test. This option is used to generate the files used for the tests."
          )
          print(
              "*********************************************************************************"
          )
  
      return results
  
imports: import GPyOpt
  import numpy
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: run_eval(problem_config, f_inits, method_config, name, outpath, time_limit, unittest) and returns: results