code: def _get_other(feature_weights, named_found_features):
      # type: (FeatureWeights, List[Tuple[str, FoundFeatures]]) -> FeatureWeights
      # search for items that were not accounted at all.
      other_items = []  # type: List[FeatureWeight]
      accounted_keys = set()  # type: Set[Tuple[str, int]]
      all_found_features = set()  # type: Set[Tuple[str, int]]
      for _, found_features in named_found_features:
          all_found_features.update(found_features)
  
      for group in ["pos", "neg"]:
          for idx, fw in enumerate(getattr(feature_weights, group)):
              key = (group, idx)
              if (
                  key not in all_found_features
                  and key not in accounted_keys
              ):
                  other_items.append(fw)
                  accounted_keys.add(key)
  
      for vec_name, found_features in named_found_features:
          if found_features:
              other_items.append(
                  FeatureWeight(
                      feature=FormattedFeatureName(
                          "{}Highlighted in text (sum)".format(
                              "{}: ".format(vec_name)
                              if vec_name
                              else ""
                          )
                      ),
                      weight=sum(found_features.values()),
                  )
              )
  
      other_items.sort(key=lambda x: abs(x.weight), reverse=True)
      return FeatureWeights(
          pos=[fw for fw in other_items if fw.weight >= 0],
          neg=[fw for fw in other_items if fw.weight < 0],
          pos_remaining=feature_weights.pos_remaining,
          neg_remaining=feature_weights.neg_remaining,
      )
  
file_path: eli5/eli5/sklearn/text.py
imports: from __future__ import absolute_import
  from typing import Any, Union, Callable, Dict, List, Optional, Set, Tuple
  from sklearn.pipeline import FeatureUnion
  from eli5.base import DocWeightedSpans, WeightedSpans, FeatureWeights, FeatureWeight, TargetExplanation
  from eli5.formatters import FormattedFeatureName
  from eli5.sklearn.unhashing import InvertableHashingVectorizer
  from eli5.sklearn._span_analyzers import build_span_analyzer
label_desc: Use image node_id 7 for calling a global function with example usage: _get_other(feature_weights, named_found_features) and returns: FeatureWeights