code: def handle_vec(
      clf, doc, vec, vectorized, feature_names, num_features=None
  ):
      # type: (...) -> Tuple[Any, FeatureNames]
      if not vectorized:
          vec = invert_hashing_and_fit(vec, [doc])
      if (
          vec is None
          and feature_names is None
          and pandas_available
          and isinstance(doc, pd.Series)
      ):
          feature_names = list(doc.index)
      # Explaining predictions does not need coef_scale
      # because it is handled by the vectorizer.
      feature_names = handle_hashing_vec(
          vec, feature_names, coef_scale=None, with_coef_scale=False
      )
      feature_names = get_feature_names(
          clf,
          vec,
          feature_names=feature_names,
          num_features=num_features,
      )
      return vec, feature_names
  
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 14 for calling a global function with example usage: handle_vec(clf, doc, vec, vectorized, feature_names, num_features) and returns: vec,  feature_names