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 predict(self, X):
      if (
          self.posthoc_ridge and self.weighted_model_
      ):  # note, during fitting don't use the weighted moel
          X_feats = self._extract_tree_predictions(X)
          return self.weighted_model_.predict(X_feats)
      preds = np.zeros(X.shape[0])
      for tree in self.trees_:
          preds += self._predict_tree(tree, X)
      if self.prediction_task == "regression":
          return preds
      elif self.prediction_task == "classification":
          return (preds > 0.5).astype(int)
  
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 9 for calling the FIGSExt obj's underlying member method code with example usage: obj.predict(X) and returns: self, preds, unknown