code: def setup(self):
      np.random.seed(42)
      random.seed(42)
      self.p = 10
      self.n = 50
      self.beta = np.array([1] + [0] * (self.p - 1))
      self.sigma = 1
      self.X = np.random.randn(self.n, self.p)
      self.y = self.X @ self.beta + self.sigma * np.random.randn(self.n)
  
      self.tree_model = DecisionTreeRegressor(max_leaf_nodes=5)
      self.tree_model.fit(self.X, self.y)
  
      self.rf_model = RandomForestRegressor(
          max_features=0.33, min_samples_leaf=5, n_estimators=5
      )
      self.rf_model.fit(self.X, self.y)
      self.n_internal_nodes = (
          self.tree_model.tree_.node_count - 1
      ) // 2
  
imports: import copy
  import numpy
  import random
  import scipy
  from sklearn.linear_model import Ridge, LinearRegression
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from sklearn.metrics import r2_score, log_loss, roc_auc_score, mean_squared_error
  from imodels.importance.block_transformers import IdentityTransformer, TreeTransformer, CompositeTransformer, MDIPlusDefaultTransformer
  from imodels.importance.ppms import RidgeRegressorPPM, LogisticClassifierPPM, RobustRegressorPPM
  from imodels.importance.mdi_plus import TreeMDIPlus, ForestMDIPlus
  from imodels.importance.rf_plus import RandomForestPlusClassifier
label_desc: Use image node_id 1 for calling the TestTransformers obj's underlying member method code with example usage: obj.setup() without return types