code: def env_4():
      return Environment(
          train_dataset=get_breast_cancer_data(target="diagnosis"),
          results_path=assets_dir,
          target_column="diagnosis",
          metrics=dict(
              roc_auc="roc_auc_score",
              f1=f1_score,
              f1_micro=lambda y_true, y_pred: f1_score(
                  y_true, y_pred, average="micro"
              ),
              f1_macro=lambda y_true, y_pred: f1_score(
                  y_true, y_pred, average="macro"
              ),
          ),
          cv_type="KFold",
          cv_params=dict(n_splits=2, shuffle=True, random_state=42),
          verbose=1,
      )
  
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 5 for calling a global function with example usage: env_4() and returns: Environment