code: def handle_hashing_vec(
      vec, feature_names, coef_scale, with_coef_scale=True
  ):
      """Return feature_names and coef_scale (if with_coef_scale is True),
      calling .get_feature_names for invhashing vectorizers.
      """
      needs_coef_scale = with_coef_scale and coef_scale is None
      if is_invhashing(vec):
          if feature_names is None:
              feature_names = vec.get_feature_names(always_signed=False)
          if needs_coef_scale:
              coef_scale = vec.column_signs_
      elif (
          isinstance(vec, FeatureUnion)
          and any(is_invhashing(v) for _, v in vec.transformer_list)
          and (needs_coef_scale or feature_names is None)
      ):
          (
              _feature_names,
              _coef_scale,
          ) = _invhashing_union_feature_names_scale(vec)
          if feature_names is None:
              feature_names = _feature_names
          if needs_coef_scale:
              coef_scale = _coef_scale
      return (
          (feature_names, coef_scale)
          if with_coef_scale
          else feature_names
      )
  
imports: from __future__ import absolute_import
  from collections import defaultdict, Counter
  from itertools import chain
  from typing import List, Iterable, Any, Dict, Tuple, Union
  import numpy
  import six
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_extraction.text import HashingVectorizer, FeatureHasher
  from sklearn.pipeline import FeatureUnion
  from eli5._feature_names import FeatureNames
label_desc: Use image node_id 6 for calling a global function with example usage: handle_hashing_vec(vec, feature_names, coef_scale, with_coef_scale) and returns: unknown