code: def _invhashing_union_feature_names_scale(vec_union):
      # type: (FeatureUnion) -> Tuple[FeatureNames, np.ndarray]
      feature_names_store = {}  # type: Dict[int, Union[str, List]]
      unkn_template = None
      shift = 0
      coef_scale_values = []
      for vec_name, vec in vec_union.transformer_list:
          if isinstance(vec, InvertableHashingVectorizer):
              vec_feature_names = vec.get_feature_names(
                  always_signed=False
              )
              unkn_template = vec_feature_names.unkn_template
              for idx, fs in vec_feature_names.feature_names.items():
                  new_fs = []
                  for f in fs:
                      new_f = dict(f)
                      new_f["name"] = "{}__{}".format(
                          vec_name, f["name"]
                      )
                      new_fs.append(new_f)
                  feature_names_store[idx + shift] = new_fs
              coef_scale_values.append((shift, vec.column_signs_))
              shift += vec_feature_names.n_features
          else:
              vec_feature_names = vec.get_feature_names()
              feature_names_store.update(
                  (shift + idx, "{}__{}".format(vec_name, fname))
                  for idx, fname in enumerate(vec_feature_names)
              )
              shift += len(vec_feature_names)
      n_features = shift
      feature_names = FeatureNames(
          feature_names=feature_names_store,
          n_features=n_features,
          unkn_template=unkn_template,
      )
      coef_scale = np.ones(n_features) * np.nan
      for idx, values in coef_scale_values:
          coef_scale[idx : idx + len(values)] = values
      return feature_names, coef_scale
  
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 7 for calling a global function with example usage: _invhashing_union_feature_names_scale(vec_union) and returns: feature_names,  coef_scale