code: def fe_optimizer(request):
      if request.param is not None:
          request.param = FeatureEngineer(request.param)
      opt = BayesianOptPro()
      opt.forge_experiment(
          model_initializer=Ridge,
          model_init_params={},
          feature_engineer=request.param,
      )
      opt.go()
      return opt
  
imports: from hyperparameter_hunter import __version__
  from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter import Categorical, Integer, BayesianOptPro, GBRT, RF, ET, DummyOptPro
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data, get_boston_data
  from hyperparameter_hunter.utils.optimization_utils import get_choice_dimensions
  from hyperparameter_hunter.utils.version_utils import HHVersion
  import numpy
  from os import makedirs
  import pandas
  import pytest
  from shutil import rmtree
  import sys
  from sklearn.ensemble import AdaBoostRegressor
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import MinMaxScaler, Normalizer, QuantileTransformer, StandardScaler
label_desc: Use image node_id 19 for calling a global function with example usage: fe_optimizer(request) and returns: opt