code: def env_3():
      def printer_callback():
          def printer_helper(
              _rep, _fold, _run, last_evaluation_results
          ):
              print(
                  f"{_rep}.{_fold}.{_run}   {last_evaluation_results}"
              )
  
          return lambda_callback(
              on_exp_start=printer_helper,
              on_exp_end=printer_helper,
              on_rep_start=printer_helper,
              on_rep_end=printer_helper,
              on_fold_start=printer_helper,
              on_fold_end=printer_helper,
              on_run_start=printer_helper,
              on_run_end=printer_helper,
          )
  
      return Environment(
          train_dataset=get_toy_classification_data(),
          results_path=assets_dir,
          metrics=["roc_auc_score"],
          holdout_dataset=get_toy_classification_data(),
          cv_type=RepeatedStratifiedKFold,
          cv_params=dict(n_splits=3, n_repeats=2, random_state=32),
          runs=2,
          experiment_callbacks=[
              printer_callback(),
              confusion_matrix_oof(),
              confusion_matrix_holdout(),
          ],
      )
  
imports: from hyperparameter_hunter import Environment, CVExperiment, Real, Integer, Categorical
  from hyperparameter_hunter import BayesianOptPro, ExtraTreesOptPro, lambda_callback
  from hyperparameter_hunter.callbacks.recipes import confusion_matrix_oof, confusion_matrix_holdout
  from hyperparameter_hunter.i_o.recorders import YAMLDescriptionRecorder, UnsortedIDLeaderboardRecorder
  from hyperparameter_hunter.i_o.result_reader import has_experiment_result_file
  from hyperparameter_hunter.utils.learning_utils import get_toy_classification_data, get_breast_cancer_data
  import pytest
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.metrics import f1_score
  from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier
label_desc: Use image node_id 4 for calling a global function with example usage: env_3() and returns: Environment, lambda_callback