code: def do_optimization(opt_pro, build_fn, ready_only=False):
      opt = opt_pro(iterations=2, random_state=32, n_initial_points=1)
      opt.forge_experiment(
          KerasClassifier,
          build_fn,
          model_extra_params=dict(batch_size=32, epochs=1, verbose=0),
      )
  
      if ready_only:
          opt.get_ready()
      else:
          opt.go()
  
      return opt
  
file_path: hyperparameter_hunter/tests/integration_tests/test_optimization_space.py
imports: from hyperparameter_hunter import __version__, Environment, Real, Integer, Categorical
  from hyperparameter_hunter import BayesianOptPro, GBRT, RF, ET, DummyOptPro
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data
  from hyperparameter_hunter.utils.version_utils import HHVersion
  from os import makedirs
  import pytest
  from shutil import rmtree
  from keras.layers import Dense, Dropout
  from keras.models import Sequential
  from keras.wrappers.scikit_learn import KerasClassifier
label_desc: Use image node_id 7 for calling a global function with example usage: do_optimization(opt_pro, build_fn, ready_only) and returns: opt