code: def get_feature_names(
      clf,
      vec=None,
      bias_name="<BIAS>",
      feature_names=None,
      num_features=None,
      estimator_feature_names=None,
  ):
      # type: (Any, Any, Optional[str], Any, int, Any) -> FeatureNames
      """
      Return a FeatureNames instance that holds all feature names
      and a bias feature.
      If vec is None or doesn't have get_feature_names() method,
      features are named x0, x1, x2, etc.
      """
      if not has_intercept(clf):
          bias_name = None
  
      if feature_names is None:
          if vec and hasattr(vec, "get_feature_names"):
              return FeatureNames(
                  vec.get_feature_names(), bias_name=bias_name
              )
          else:
              if estimator_feature_names is None:
                  num_features = num_features or get_num_features(clf)
                  return FeatureNames(
                      n_features=num_features,
                      unkn_template="x%d",
                      bias_name=bias_name,
                  )
              return FeatureNames(
                  estimator_feature_names, bias_name=bias_name
              )
  
      num_features = num_features or get_num_features(clf)
      if isinstance(feature_names, FeatureNames):
          if feature_names.n_features != num_features:
              raise ValueError(
                  "feature_names has a wrong n_features: "
                  "expected=%d, got=%d"
                  % (num_features, feature_names.n_features)
              )
          # Make a shallow copy setting proper bias_name
          return FeatureNames(
              feature_names.feature_names,
              n_features=num_features,
              bias_name=bias_name,
              unkn_template=feature_names.unkn_template,
          )
      else:
          if len(feature_names) != num_features:
              raise ValueError(
                  "feature_names has a wrong length: "
                  "expected=%d, got=%d"
                  % (num_features, len(feature_names))
              )
          return FeatureNames(feature_names, bias_name=bias_name)
  
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 6 for calling a global function with example usage: get_feature_names(clf, vec, bias_name, feature_names, num_features, estimator_feature_names) and returns: FeatureNames, FeatureNames, FeatureNames, FeatureNames, FeatureNames