code: def explain_rf_feature_importance(
      estimator,
      vec=None,
      top=_TOP,
      target_names=None,  # ignored
      targets=None,  # ignored
      feature_names=None,
      feature_re=None,
      feature_filter=None,
  ):
      """
      Return an explanation of a tree-based ensemble estimator.
  
      See :func:`eli5.explain_weights` for description of
      ``top``, ``feature_names``, ``feature_re`` and ``feature_filter``
      parameters.
  
      ``target_names`` and ``targets`` parameters are ignored.
  
      ``vec`` is a vectorizer instance used to transform
      raw features to the input of the estimator (e.g. a fitted
      CountVectorizer instance); you can pass it instead of ``feature_names``.
      """
      coef = estimator.feature_importances_
      trees = np.array(estimator.estimators_).ravel()
      coef_std = np.std(
          [tree.feature_importances_ for tree in trees], axis=0
      )
      return get_feature_importance_explanation(
          estimator,
          vec,
          coef,
          coef_std=coef_std,
          feature_names=feature_names,
          feature_filter=feature_filter,
          feature_re=feature_re,
          top=top,
          description=DESCRIPTION_RANDOM_FOREST,
          is_regression=isinstance(estimator, RegressorMixin),
      )
  
imports: from __future__ import absolute_import
  import numpy
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.pipeline import Pipeline
  from sklearn.linear_model import ElasticNet, ElasticNetCV, HuberRegressor, Lars, LassoCV, LinearRegression, LogisticRegression, LogisticRegressionCV, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, PassiveAggressiveClassifier, PassiveAggressiveRegressor, Perceptron, Ridge, RidgeClassifier, RidgeClassifierCV, RidgeCV, SGDClassifier, SGDRegressor, TheilSenRegressor
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.svm import LinearSVC, LinearSVR, SVC, SVR, NuSVC, NuSVR, OneClassSVM
  from sklearn.naive_bayes import BernoulliNB, MultinomialNB
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, AdaBoostClassifier, AdaBoostRegressor, RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from eli5.base import Explanation, TargetExplanation, FeatureImportances
  from eli5.base_utils import singledispatch
  from eli5._feature_weights import get_top_features
  from eli5.utils import argsort_k_largest_positive, get_target_display_names
  from eli5.sklearn.unhashing import handle_hashing_vec, is_invhashing
  from eli5.sklearn.treeinspect import get_tree_info
  from eli5.sklearn.utils import get_coef, is_multiclass_classifier, is_multitarget_regressor, get_feature_names, get_feature_names_filtered, get_default_target_names
  from eli5.explain import explain_weights
  from eli5.transform import transform_feature_names
  from eli5._feature_importances import get_feature_importances_filtered, get_feature_importance_explanation
  from .permutation_importance import PermutationImportance
label_desc: Use image node_id 8 for calling a global function with example usage: explain_rf_feature_importance(estimator, vec, top, target_names, targets, feature_names, feature_re, feature_filter) and returns: get_feature_importance_explanation