class_docstr: Class for recovering a mapping used by FeatureHasher.
code: def _get_collision_info(self):
      # type: () -> Tuple[List[int], List[np.ndarray], List[np.ndarray]]
      column_ids, term_names, term_signs = [], [], []
      for column_id, _term_ids in self.collisions_.items():
          column_ids.append(column_id)
          term_names.append(self.terms_[_term_ids])
          term_signs.append(self.term_signs_[_term_ids])
      return column_ids, term_names, term_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 7 for calling the FeatureUnhasher obj's underlying member method code with example usage: obj._get_collision_info() and returns: column_ids,  term_names,  term_signs