class_docstr: Class for recovering a mapping used by FeatureHasher.
code: def _get_column_signs(self):
      colums_signs = np.ones(self.n_features) * np.nan
      for hash_id, term_ids in self.collisions_.items():
          term_signs = self.term_signs_[term_ids]
          if _invert_signs(term_signs):
              colums_signs[hash_id] = -1
          elif (term_signs > 0).all():
              colums_signs[hash_id] = 1
      return colums_signs
  
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 the FeatureUnhasher obj's underlying member method code with example usage: obj._get_column_signs() and returns: colums_signs