code: def __str__(self):
      # check if fitted
      if not checks.check_is_fitted(self.estimator_):
          s = self.__class__.__name__
          s += "("
          s += "est="
          s += repr(self.estimator_)
          s += ", "
          s += "reg_param="
          s += str(self.reg_param)
          s += ")"
          return s
      else:
          s = "> ------------------------------\n"
          s += "> Decision Tree with Hierarchical Shrinkage\n"
          s += "> \tPrediction is made by looking at the value in the appropriate leaf of the tree\n"
          s += "> ------------------------------" + "\n"
  
          if (
              hasattr(self, "feature_names")
              and self.feature_names is not None
          ):
              return s + export_text(
                  self.estimator_,
                  feature_names=self.feature_names,
                  show_weights=True,
              )
          else:
              return s + export_text(self.estimator_, show_weights=True)
  
imports: import time
  from copy import deepcopy
  from typing import List
  import numpy
  from sklearn import datasets
  from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
  from sklearn.metrics import r2_score, mean_squared_error, log_loss
  from sklearn.model_selection import cross_val_score, KFold
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, export_text
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, RandomForestRegressor
  from imodels.util import checks
  from imodels.util.arguments import check_fit_arguments
  from imodels.util.tree import compute_tree_complexity
label_desc: Use image node_id 9 for calling the HSTree obj's underlying member method code with example usage: obj.__str__() and returns: s, unknown, unknown