code: def env_boston():
      return Environment(
          train_dataset=get_boston_data(),
          results_path=assets_dir,
          target_column="DIS",
          metrics=["r2_score"],
          cv_type="KFold",
          cv_params=dict(n_splits=3, random_state=1),
      )
  
imports: from hyperparameter_hunter import Environment, FeatureEngineer, EngineerStep, __version__
  from hyperparameter_hunter import CVExperiment, Real, Integer, Categorical
  from hyperparameter_hunter import BayesianOptPro, DummyOptPro, ExtraTreesOptPro
  from hyperparameter_hunter.utils.learning_utils import get_boston_data
  from hyperparameter_hunter.utils.version_utils import HHVersion
  from os import makedirs
  import pytest
  from shutil import rmtree
  from xgboost import XGBRegressor
label_desc: Use image node_id 2 for calling a global function with example usage: env_boston() and returns: Environment