code: def boston_env():
      return Environment(
          train_dataset=boston_head,
          holdout_dataset=holdout_last_row,
          target_column="DIS",
          metrics=["r2_score", "median_absolute_error"],
          cv_type="KFold",
          cv_params=dict(n_splits=3, random_state=1),
          experiment_callbacks=[dataset_recorder()],
      )
  
file_path: hyperparameter_hunter/tests/integration_tests/feature_engineering/test_intra_cv_target_transform.py
imports: from hyperparameter_hunter import Environment, CVExperiment
  from hyperparameter_hunter.callbacks.recipes import dataset_recorder
  from hyperparameter_hunter.feature_engineering import FeatureEngineer
  from numpy.testing import assert_array_almost_equal
  import pandas
  import pytest
  from sklearn.linear_model import Ridge
  from sklearn.preprocessing import QuantileTransformer
label_desc: Use image node_id 3 for calling a global function with example usage: boston_env() and returns: Environment