code: def _create_importances_bunch(baseline_score, permuted_score):
      """Compute the importances as the decrease in score.
  
      Parameters
      ----------
      baseline_score : ndarray of shape (n_features,)
          The baseline score without permutation.
      permuted_score : ndarray of shape (n_features, n_repeats)
          The permuted scores for the `n` repetitions.
  
      Returns
      -------
      importances : :class:`~sklearn.utils.Bunch`
          Dictionary-like object, with the following attributes.
          importances_mean : ndarray, shape (n_features, )
              Mean of feature importance over `n_repeats`.
          importances_std : ndarray, shape (n_features, )
              Standard deviation over `n_repeats`.
          importances : ndarray, shape (n_features, n_repeats)
              Raw permutation importance scores.
      """
      importances = baseline_score - permuted_score
      return Bunch(
          importances_mean=np.mean(importances, axis=1),
          importances_std=np.std(importances, axis=1),
          importances=importances,
      )
  
file_path: scikit-learn/sklearn/inspection/_permutation_importance.py
imports: import numbers
  import numpy
  from ..ensemble._bagging import _generate_indices
  from ..metrics import check_scoring, get_scorer_names
  from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer
  from ..model_selection._validation import _aggregate_score_dicts
  from ..utils import Bunch, _safe_indexing, check_array, check_random_state
  from ..utils._param_validation import HasMethods, Integral, Interval, RealNotInt, StrOptions, validate_params
  from ..utils.parallel import Parallel, delayed
label_desc: Use image node_id 3 for calling a global function with example usage: _create_importances_bunch(baseline_score, permuted_score) and returns: Bunch