code: def get_feature_names_filtered(
      clf,
      vec=None,
      bias_name="<BIAS>",
      feature_names=None,
      num_features=None,
      feature_filter=None,
      feature_re=None,
      estimator_feature_names=None,
  ):
      # type: (...) -> Tuple[FeatureNames, List[int]]
      feature_names = get_feature_names(
          clf=clf,
          vec=vec,
          bias_name=bias_name,
          feature_names=feature_names,
          num_features=num_features,
          estimator_feature_names=estimator_feature_names,
      )
      return feature_names.handle_filter(feature_filter, feature_re)
  
imports: from __future__ import absolute_import
  from distutils.version import LooseVersion
  from typing import Any, Optional, List, Tuple
  import numpy
  import scipy.sparse
  from sklearn.multiclass import OneVsRestClassifier
  from eli5.sklearn.unhashing import invert_hashing_and_fit, handle_hashing_vec
  from eli5._feature_names import FeatureNames
label_desc: Use image node_id 7 for calling a global function with example usage: get_feature_names_filtered(clf, vec, bias_name, feature_names, num_features, feature_filter, feature_re, estimator_feature_names) and returns: feature_names