docstr: For each selected base estimator, average models trained on each fold
code: def _create_cv_ensemble(
      self, base_ensemble, idx_models_included, model_names=None
  ):
      """For each selected base estimator, average models trained on each fold"""
      fitted_models = np.empty(len(idx_models_included), dtype=object)
      for i, idx in enumerate(idx_models_included):
          model_name = (
              self.base_estimators[idx][0]
              if model_names is None
              else model_names[idx]
          )
          avg_model = EnsembleAverage(
              base_ensemble[idx, :], name=model_name
          )
          fitted_models[i] = avg_model
  
      return fitted_models
  
imports: import numbers
  from joblib import Parallel, delayed
  import numpy
  from scipy.stats import kendalltau, rankdata, spearmanr
  from sklearn.base import BaseEstimator, clone
  from sklearn.model_selection import check_cv
  from sklearn.utils._param_validation import Interval, StrOptions
  from .base import _fit_and_score
  from .stacking import Stacking
label_desc: Use image node_id 5 for calling the BaseEnsembleSelection obj's underlying member method code with example usage: obj._create_cv_ensemble(base_ensemble, idx_models_included, model_names) and returns: fitted_models