code: def _recover_feature_importance(model_dict):
      feature_importance = {}
      for k, v in model_dict["feature_importance"].items():
          feature_importance[k] = FeatureImportance.from_dict(v)
      return feature_importance
  
imports: import numpy
  import pandas
  from fate.arch import Context
  from fate.arch.dataframe import DataFrame
  from fate.ml.ensemble.learner.decision_tree.tree_core.splitter import SplitInfo
  from typing import List
  import logging
label_desc: Use image node_id 20 for calling the DecisionTree obj's underlying member method code with example usage: obj._recover_feature_importance(model_dict) and returns: feature_importance