code: def _get_decision_path(leaf_index, split_index, leaf_id):
      path, split_features = [], []
      parent_id, leaf = leaf_index[leaf_id]
      path.append(leaf["leaf_value"])
      while True:
          if parent_id == -1:
              break
          parent_id, node = split_index[parent_id]
          path.append(node["_node_value"])
          split_features.append(node["split_feature"])
  
      path.reverse()
      changes = _changes(path)
      bias, path = changes[0], list(
          zip(reversed(split_features), changes[1:])
      )
      return bias, path
  
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 6 for calling a global function with example usage: _get_decision_path(leaf_index, split_index, leaf_id) and returns: bias,  path