code: def _get_collisions(indices):
      # type: (...) -> Dict[int, List[int]]
      """
      Return a dict ``{column_id: [possible term ids]}``
      with collision information.
      """
      collisions = defaultdict(list)  # type: Dict[int, List[int]]
      for term_id, hash_id in enumerate(indices):
          collisions[hash_id].append(term_id)
      return dict(collisions)
  
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 1 for calling a global function with example usage: _get_collisions(indices) and returns: dict