class_docstr: FIGSExt (sum of trees) classifier.
  Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models.
  Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation.
  The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable.
  Experiments across a wide array of real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).
  https://arxiv.org/abs/2201.11931
code: def __str__(self):
      s = "------------\n" + "\n\t+\n".join(
          [self._tree_to_str(t) for t in self.trees_]
      )
      if (
          hasattr(self, "feature_names_")
          and self.feature_names_ is not None
      ):
          for i in range(len(self.feature_names_))[::-1]:
              s = s.replace(f"X_{i}", self.feature_names_[i])
      return s
  
imports: from copy import deepcopy
  import numpy
  from matplotlib import pyplot
  from sklearn import datasets
  from sklearn import tree
  from sklearn.base import BaseEstimator
  from sklearn.linear_model import RidgeCV, RidgeClassifierCV
  from sklearn.model_selection import train_test_split
  from sklearn.tree import plot_tree
  from sklearn.utils import check_X_y
  from imodels.tree.viz_utils import extract_sklearn_tree_from_figs
label_desc: Use image node_id 8 for calling the FIGSExt obj's underlying member method code with example usage: obj.__str__() and returns: s