code: def test_find_optimum_trajectories(setup_input, expected_sample):
      N = 6
      problem = {"num_vars": 2, "names": ["x1", "x2"], "groups": None}
      k_choices = 4
  
      problem = _define_problem_with_groups(problem)
  
      output = _compute_optimised_trajectories(
          problem, setup_input, N, k_choices
      )
      expected = expected_sample
      np.testing.assert_equal(output, expected)
  
imports: from numpy.testing import assert_equal, assert_allclose
  from pytest import raises, fixture, warns, mark
  import numpy
  import warnings
  from SALib.sample.morris.morris import sample, _check_group_membership, _check_if_num_levels_is_even, _compute_optimised_trajectories, _generate_p_star, _compute_b_star, _generate_trajectory, _generate_x_star
  from SALib.util import read_param_file, compute_groups_matrix, _define_problem_with_groups, _compute_delta
label_desc: Use image node_id 9 for calling a global function with example usage: test_find_optimum_trajectories(setup_input, expected_sample) without return types