code: def get_feature_importance_explanation(
      estimator,
      vec,
      coef,
      feature_names,
      feature_filter,
      feature_re,
      top,
      description,
      is_regression,
      estimator_feature_names=None,
      num_features=None,
      coef_std=None,
  ):
      # type: (...) -> Explanation
      feature_names, flt_indices = get_feature_names_filtered(
          estimator,
          vec,
          feature_names=feature_names,
          estimator_feature_names=estimator_feature_names,
          feature_filter=feature_filter,
          feature_re=feature_re,
          num_features=num_features,
      )
      feature_importances = get_feature_importances_filtered(
          coef, feature_names, flt_indices, top, coef_std
      )
      return Explanation(
          feature_importances=feature_importances,
          description=description,
          estimator=repr(estimator),
          method="feature importances",
          is_regression=is_regression,
      )
  
imports: from eli5.base import FeatureImportances, Explanation
  from eli5.sklearn.utils import get_feature_names_filtered
  from eli5.utils import argsort_k_largest
label_desc: Use image node_id 2 for calling a global function with example usage: get_feature_importance_explanation(estimator, vec, coef, feature_names, feature_filter, feature_re, top, description, is_regression, estimator_feature_names, num_features, coef_std) and returns: Explanation