class_docstr: Unittest for the available acquisition functions optimizers
code: def setUp(self):
      ##
      # -- methods configuration
      ##
  
      model_type = "GP"
      initial_design_numdata = None
      initial_design_type = "random"
      acquisition_type = "EI"
      normalize_Y = True
      exact_feval = True
      acquisition_optimizer_type = "lbfgs"
      model_update_interval = 1
      evaluator_type = "sequential"
      batch_size = 1
      num_cores = 1
      verbosity = False
  
      # stop conditions
      max_iter = 5
      max_time = 999
      eps = 1e-8
  
      self.methods_configs = [
          {
              "name": "lbfgs",
              "model_type": model_type,
              "initial_design_numdata": initial_design_numdata,
              "initial_design_type": initial_design_type,
              "acquisition_type": acquisition_type,
              "normalize_Y": normalize_Y,
              "exact_feval": exact_feval,
              "acquisition_optimizer_type": "lbfgs",
              "model_update_interval": model_update_interval,
              "verbosity": verbosity,
              "evaluator_type": evaluator_type,
              "batch_size": batch_size,
              "num_cores": num_cores,
              "max_iter": max_iter,
              "max_time": max_time,
              "eps": eps,
          },
          # {   'name': 'DIRECT',
          #     'model_type'                 : model_type,
          #     'initial_design_numdata'     : initial_design_numdata,
          #     'initial_design_type'        : initial_design_type,
          #     'acquisition_type'           : acquisition_type,
          #     'normalize_Y'                : normalize_Y,
          #     'exact_feval'                : exact_feval,
          #     'acquisition_optimizer_type' : 'DIRECT',
          #     'model_update_interval'      : model_update_interval,
          #     'verbosity'                  : verbosity,
          #     'evaluator_type'             : evaluator_type,
          #     'batch_size'                 : batch_size,
          #     'num_cores'                  : num_cores,
          #     'max_iter'                   : 1,
          #     'max_time'                   : max_time,
          #     'eps'                        : eps
          #     },
          # {   'name': 'CMA',
          #     'model_type'                 : model_type,
          #     'initial_design_numdata'     : initial_design_numdata,
          #     'initial_design_type'        : initial_design_type,
          #     'acquisition_type'           : acquisition_type,
          #     'normalize_Y'                : normalize_Y,
          #     'exact_feval'                : exact_feval,
          #     'acquisition_optimizer_type' : 'CMA',
          #     'model_update_interval'      : model_update_interval,
          #     'verbosity'                  : verbosity,
          #     'evaluator_type'             : evaluator_type,
          #     'batch_size'                 : batch_size,
          #     'num_cores'                  : num_cores,
          #     'max_iter'                   : max_iter,
          #     'max_time'                   : max_time,
          #     'eps'                        : eps
          #     }
      ]
  
      # -- Problem setup
  
      n_inital_design = 5
      input_dim = 5
      f_bounds = (-5, 5)
      np.random.seed(1)
      self.f_inits = samples_multidimensional_uniform(
          [f_bounds] * input_dim, n_inital_design
      )
      self.f_inits = self.f_inits.reshape(
          input_dim, self.f_inits.shape[-1]
      )
  
      self.problem_config = {
          "objective": GPyOpt.objective_examples.experimentsNd.gSobol(
              np.ones(input_dim)
          ).f,
          "domain": [
              {
                  "name": "var_1",
                  "type": "continuous",
                  "domain": f_bounds,
                  "dimensionality": input_dim,
              }
          ],
          "constraints": None,
          "cost_withGradients": None,
      }
  
file_path: GPyOpt/GPyOpt/testing/functional_tests/test_acquisitions_optimizers.py
imports: import numpy
  import GPyOpt
  from GPyOpt.util.general import samples_multidimensional_uniform
  from base_test_case import BaseTestCase
label_desc: Use image node_id 1 for calling the TestAcquisitions obj's underlying member method code with example usage: obj.setUp() without return types