code: def __init__(
      self,
      estimator_: BaseEstimator = DecisionTreeClassifier(
          max_leaf_nodes=20
      ),
      reg_param: float = 1,
      shrinkage_scheme_: str = "node_based",
      max_leaf_nodes: int = None,
      random_state: int = None,
  ):
      super().__init__(
          estimator_=estimator_,
          reg_param=reg_param,
          shrinkage_scheme_=shrinkage_scheme_,
          max_leaf_nodes=max_leaf_nodes,
          random_state=random_state,
      )
  
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 1 to create a new HSTreeClassifier object from inherited base classes: HSTree, ClassifierMixin with example: obj = HSTreeClassifier(estimator_, reg_param, shrinkage_scheme_, max_leaf_nodes, random_state)