code: def sorted_for_ner(crf_classes):
      """
      Return labels sorted in a default order suitable for NER tasks:
  
      >>> sorted_for_ner(['B-ORG', 'B-PER', 'O', 'I-PER'])
      ['O', 'B-ORG', 'B-PER', 'I-PER']
      """
  
      def key(cls):
          if len(cls) > 2 and cls[1] == "-":
              # group names like B-ORG and I-ORG together
              return cls.split("-", 1)[1], cls
          return "", cls
  
      return sorted(crf_classes, key=key)
  
file_path: eli5/eli5/sklearn_crfsuite/explain_weights.py
imports: from __future__ import absolute_import
  import numpy
  from scipy import sparse
  from sklearn_crfsuite import CRF
  from eli5.base import Explanation, TargetExplanation, TransitionFeatureWeights
  from eli5.explain import explain_weights
  from eli5.utils import get_target_display_names
  from eli5._feature_names import FeatureNames
  from eli5._feature_weights import get_top_features
label_desc: Use image node_id 5 for calling a global function with example usage: sorted_for_ner(crf_classes) and returns: sorted, str,  cls, cls,  cls