class_docstr: Robustness verification for decision-tree-based models.
  Following the implementation in https://github.com/chenhongge/treeVerification (MIT License, 9 August 2019)
  
  | Paper link: https://arxiv.org/abs/1906.03849
docstr: Get the list of best scores.
  
  :param i_sample: Index of training sample in `x`.
  :param eps: Attack budget epsilon.
  :param norm: The norm to apply epsilon.
  :param target_label: The target label.
  :return: The best scores.
docstr_tok: ["Get","the","list","of","best","scores",".",":","param","i_sample",":","Index","of","training","sample","in","`","x","`",".",":","param","eps",":","Attack","budget","epsilon",".",":","param","norm",":","The","norm","to","apply","epsilon",".",":","param","target_label",":","The","target","label",".",":","return",":","The","best","scores","."]
code: def _get_best_score(
      self,
      i_sample: int,
      eps: float,
      norm: float,
      target_label: Optional[int],
  ) -> float:
      """
      Get the list of best scores.
  
      :param i_sample: Index of training sample in `x`.
      :param eps: Attack budget epsilon.
      :param norm: The norm to apply epsilon.
      :param target_label: The target label.
      :return: The best scores.
      """
      nodes = self._get_accessible_leaves(
          i_sample, eps, norm, target_label
      )
      best_score: float = 0.0
  
      for i_level in range(self.max_level):
          if self._classifier.nb_classes > 2 and i_level > 0:
              target_label = None
          best_score, nodes = self._get_k_partite_clique(
              nodes, label=self.y[i_sample], target_label=target_label
          )
  
          # Stop if the root node has been reached
          if len(nodes) <= 1:
              break
  
      return best_score
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from tqdm.auto import trange
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 4 for calling the RobustnessVerificationTreeModelsCliqueMethod obj's underlying member method code with example usage: obj._get_best_score(i_sample, eps, norm, target_label) and returns: best_score