docstr: Test whether finding the local maximum distance equals the global
  maximum distance in a simple case for a defined random seed.
  From Saltelli et al. 2008, in the solution to exercise 3a,
  Chapter 3, page 134.
  
  Note that local and brute force methods are not guaranteed to produce
  the same results, even for simple problems,
  hence forcing the seed here.
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code: def test_find_local_maximum_distance(self, setup_input):
      """
      Test whether finding the local maximum distance equals the global
      maximum distance in a simple case for a defined random seed.
      From Saltelli et al. 2008, in the solution to exercise 3a,
      Chapter 3, page 134.
  
      Note that local and brute force methods are not guaranteed to produce
      the same results, even for simple problems,
      hence forcing the seed here.
  
      """
  
      rd.seed(12345)
  
      local_strategy = LocalOptimisation()
      brute_strategy = BruteForce()
  
      sample_inputs = setup_input
      N = 6
      num_params = 2
      k_choices = 4
      output_global = brute_strategy.brute_force_most_distant(
          sample_inputs, N, num_params, k_choices
      )
      output_local = local_strategy.find_local_maximum(
          sample_inputs, N, num_params, k_choices
      )
      assert_equal(output_global, output_local)
  
imports: from SALib.sample.morris import SampleMorris, LocalOptimisation, BruteForce
  from SALib.sample.morris.morris import _sample_morris
  from SALib.util import read_param_file
  import numpy
  from numpy.testing import assert_equal, assert_allclose
  import numpy.random
  import pytest
  from pytest import fixture, raises
label_desc: Use image node_id 2 for calling the TestLocallyOptimalStrategy obj's underlying member method code with example usage: obj.test_find_local_maximum_distance(setup_input) without return types