code: def _fit_invhashing_union(vec_union, docs):
      # type: (FeatureUnion, Any) -> FeatureUnion
      """Fit InvertableHashingVectorizer on doc inside a FeatureUnion."""
      return FeatureUnion(
          [
              (name, invert_hashing_and_fit(v, docs))
              for name, v in vec_union.transformer_list
          ],
          transformer_weights=vec_union.transformer_weights,
          n_jobs=vec_union.n_jobs,
      )
  
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 9 for calling a global function with example usage: _fit_invhashing_union(vec_union, docs) and returns: FeatureUnion