code: def do_optimization():
      optimizer = BayesianOptPro(iterations=5, random_state=1337)
      optimizer.forge_experiment(
          model_initializer=XGBClassifier,
          model_init_params=dict(
              objective="reg:linear",
              max_depth=Integer(2, 20),
              learning_rate=Real(0.0001, 0.5),
              subsample=0.5,
              booster=Categorical(["gbtree", "dart"]),
          ),
      )
      optimizer.go()
  
file_path: hyperparameter_hunter/examples/advanced_examples/recorder_example.py
imports: from hyperparameter_hunter import Environment, BayesianOptPro, Real, Integer, Categorical
  from hyperparameter_hunter.i_o.recorders import UnsortedIDLeaderboardRecorder, BaseRecorder
  from hyperparameter_hunter.utils.file_utils import make_dirs, read_json
  import pandas
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import StratifiedKFold
  from xgboost import XGBClassifier
  import yaml
label_desc: Use image node_id 1 for calling a global function with example usage: do_optimization() without return types