code: def from_model(self, model: dict):
      trees = model["trees"]
      self._saved_tree = trees
      self._trees = [
          HeteroDecisionTreeGuest.from_model(tree) for tree in trees
      ]
      hyper_parameter = model["hyper_param"]
  
      # these parameters are related to predict
      self.learning_rate = hyper_parameter["learning_rate"]
      self.num_class = hyper_parameter["num_class"]
      self.objective = hyper_parameter["objective"]
      self._init_score = (
          float(model["init_score"])
          if model["init_score"] is not None
          else None
      )
      # initialize
      self._tree_dim = (
          self.num_class if self.objective == MULTI_CE else 1
      )
      self._loss_func = _get_loss_func(
          self.objective, class_num=self.num_class
      )
      # for warmstart
      self._model_loaded = True
      # load loss
      self._loss_history.extend(model["loss_history"])
      # load feature importances
      self._load_feature_importance(model["feature_importance"])
  
      return self
  
imports: import numpy
  import pandas
  from typing import Optional, Union
  from fate.arch import Context
  from fate.arch.dataframe import DataFrame
  from fate.ml.ensemble.algo.secureboost.hetero._base import HeteroBoostingTree
  from fate.ml.ensemble.learner.decision_tree.hetero.guest import HeteroDecisionTreeGuest
  from fate.ml.ensemble.utils.binning import binning
  from fate.ml.ensemble.learner.decision_tree.tree_core.loss import OBJECTIVE, get_task_info, MULTI_CE, BINARY_BCE, REGRESSION_L2
  from fate.ml.ensemble.algo.secureboost.common.predict import predict_leaf_guest
  from fate.ml.utils.predict_tools import compute_predict_details, PREDICT_SCORE, BINARY, MULTI, REGRESSION
  from fate.ml.ensemble.learner.decision_tree.tree_core.decision_tree import GUEST_FEAT_ONLY, ALL_FEAT
  from fate.ml.ensemble.utils.sample import goss_sample
  import logging
label_desc: Use image node_id 13 for calling the HeteroSecureBoostGuest obj's underlying member method code with example usage: obj.from_model(model) and returns: self