code: def execute():
      env = Environment(
          train_dataset=get_toy_classification_data(),
          results_path="HyperparameterHunterAssets",
          metrics=["roc_auc_score"],
          cv_type=RepeatedStratifiedKFold,
          cv_params=dict(n_splits=5, n_repeats=2, random_state=32),
          runs=2,
          # Just instantiate `Environment` with your list of callbacks, and go about business as usual
          experiment_callbacks=[
              printer_callback(),
              confusion_matrix_oof(),
          ],
          # In addition to `printer_callback` made above, we're also adding the `confusion_matrix_oof` callback
          # This, and other callbacks, can be found in `hyperparameter_hunter.callbacks.recipes`
      )
  
      experiment = CVExperiment(
          model_initializer=XGBClassifier,
          model_init_params={},
          model_extra_params=dict(fit=dict(verbose=False)),
      )
  
file_path: hyperparameter_hunter/examples/advanced_examples/lambda_callback_example.py
imports: from hyperparameter_hunter import Environment, CVExperiment
  from hyperparameter_hunter.callbacks.bases import lambda_callback
  from hyperparameter_hunter.utils.learning_utils import get_toy_classification_data
  from hyperparameter_hunter.callbacks.recipes import confusion_matrix_oof
  from sklearn.model_selection import RepeatedStratifiedKFold
  from xgboost import XGBClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: execute() without return types