code: def recover_feature_bins(
      self,
      statistic_histogram: DistributedHistogram,
      nid_split_id: Dict[int, int],
      node_map: dict,
  ) -> Dict[int, int]:
      if self.random_seed is None:
          return nid_split_id  # randome seed has no shuffle, no need to recover
      else:
          reverse_node_map = {v: k for k, v in node_map.items()}
          nid_split_id_ = {
              node_map[k]: v for k, v in nid_split_id.items()
          }
          recover = statistic_histogram.recover_feature_bins(
              self.feat_bin_num, nid_split_id_
          )
          recover_rs = {
              reverse_node_map[k]: v for k, v in recover.items()
          }
          return recover_rs
  
imports: import typing
  import torch
  from typing import Dict
  from fate.arch.histogram import HistogramBuilder, DistributedHistogram
  from fate.ml.ensemble.learner.decision_tree.tree_core.decision_tree import Node
  from typing import List
  import numpy
  from fate.arch.dataframe import DataFrame
  from fate.arch import Context
  import logging
label_desc: Use image node_id 9 for calling the SBTHistogramBuilder obj's underlying member method code with example usage: obj.recover_feature_bins(statistic_histogram, nid_split_id, node_map) and returns: nid_split_id, recover_rs