code: def test_all_wrangled_targets(
      boston_env, engineer_experiment, exp_train, exp_oof, exp_holdout
  ):
      agg_datasets = engineer_experiment.stat_aggregates["_datasets"]
      d_pairs = [
          ("data_train", exp_train),
          ("data_oof", exp_oof),
          ("data_holdout", exp_holdout),
      ]
  
      for f, data in enumerate(agg_datasets["on_fold_start"]):
          for actual, expected in d_pairs:
              assert_array_almost_equal(
                  data[actual].input.fold, expected[0][0][f]
              )
              assert_array_almost_equal(
                  data[actual].target.fold, expected[1][0][f]
              )
              assert_array_almost_equal(
                  data[actual].input.T.fold, expected[0][1][f]
              )
              assert_array_almost_equal(
                  data[actual].target.T.fold, expected[1][1][f]
              )
  
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 5 for calling a global function with example usage: test_all_wrangled_targets(boston_env, engineer_experiment, exp_train, exp_oof, exp_holdout) without return types