code: def _get_lgb_feature_importances(lgb, importance_type):
      aliases = {"weight": "split"}
      coef = lgb.booster_.feature_importance(
          importance_type=aliases.get(importance_type, importance_type)
      )
      norm = coef.sum()
      return coef / norm if norm else coef
  
file_path: eli5/eli5/lightgbm.py
imports: from __future__ import absolute_import, division
  from collections import defaultdict
  from typing import DefaultDict, Optional
  import numpy
  import lightgbm
  from eli5.explain import explain_weights, explain_prediction
  from eli5._feature_importances import get_feature_importance_explanation
  from eli5.sklearn.utils import handle_vec, get_X, get_X0, add_intercept, predict_proba
  from eli5._decision_path import get_decision_path_explanation
label_desc: Use image node_id 4 for calling a global function with example usage: _get_lgb_feature_importances(lgb, importance_type) and returns: unknown