class_docstr: Implements the brute force optimisation strategy
docstr: Finds the `k_choices` maximum scores from `scores`
  
  Parameters
  ----------
  scores : numpy.ndarray
  N : int
  k_choices : int
  
  Returns
  -------
  list
docstr_tok: ["Finds","the","`","k_choices","`","maximum","scores","from","`","scores","`","Parameters","--","--","--","--","--","scores",":","numpy.ndarray","N",":","int","k_choices",":","int","Returns","--","--","--","-","list"]
code: def find_maximum(self, scores, N, k_choices):
      """Finds the `k_choices` maximum scores from `scores`
  
      Parameters
      ----------
      scores : numpy.ndarray
      N : int
      k_choices : int
  
      Returns
      -------
      list
      """
      if not isinstance(scores, np.ndarray):
          raise TypeError("Scores input is not a numpy array")
  
      index_of_maximum = int(scores.argmax())
      maximum_combo = self.nth(
          combinations(range(N), k_choices), index_of_maximum, None
      )
      return sorted(maximum_combo)
  
file_path: SALib/src/SALib/sample/morris/brute.py
imports: from SALib.sample.morris.strategy import Strategy
  from scipy.special import comb
  from itertools import combinations, islice
  import sys
  import numpy
  from typing import List
label_desc: Use image node_id 6 for calling the BruteForce obj's underlying member method code with example usage: obj.find_maximum(scores, N, k_choices) and returns: sorted