class_docstr: Implements the local optimisation algorithm using the Strategy interface
class_docstr_tok: ["Implements","the","local","optimisation","algorithm","using","the","Strategy","interface"]
docstr: Get the indices that belong to the maximum distance in `distances`
  
  Parameters
  ----------
  indices_list : list
      list of tuples
  distances : numpy.ndarray
      size M
  i : int
  m : int
  
  Returns
  -------
  list
docstr_tok: ["Get","the","indices","that","belong","to","the","maximum","distance","in","`","distances","`","Parameters","--","--","--","--","--","indices_list",":","list","list","of","tuples","distances",":","numpy.ndarray","size","M","i",":","int","m",":","int","Returns","--","--","--","-","list"]
code: def get_max_sum_ind(
      self,
      indices_list: List[Tuple],
      distances: np.ndarray,
      i: Union[str, int],
      m: Union[str, int],
  ) -> Tuple:
      """Get the indices that belong to the maximum distance in `distances`
  
      Parameters
      ----------
      indices_list : list
          list of tuples
      distances : numpy.ndarray
          size M
      i : int
      m : int
  
      Returns
      -------
      list
      """
      if len(indices_list) != len(distances):
          msg = (
              "Indices and distances are lists of different length."
              + "Length indices_list = {} and length distances = {}."
              + "In loop i = {}  and m =  {}"
          )
          raise ValueError(
              msg.format(len(indices_list), len(distances), i, m)
          )
  
      max_index = distances.argsort()[-1:][::-1]
      return tuple(indices_list[max_index[0]])
  
file_path: SALib/src/SALib/sample/morris/local.py
imports: from itertools import combinations
  import numpy
  from typing import List, Tuple, Union
  from .strategy import Strategy
label_desc: Use image node_id 4 for calling the LocalOptimisation obj's underlying member method code with example usage: obj.get_max_sum_ind(indices_list, distances, i, m) and returns: tuple