code: def setup_problem(setup_input):
      input_sample = setup_input
      num_samples = 6
      problem = {"num_vars": 2, "groups": None}
      k_choices = 4
  
      groups = None
      num_params = problem.get("num_vars")
  
      input_1 = [[0, 1 / 3.0], [0, 1.0], [2 / 3.0, 1.0]]
      input_3 = [[2 / 3.0, 0], [2 / 3.0, 2 / 3.0], [0, 2 / 3.0]]
      input_4 = [[1 / 3.0, 1.0], [1.0, 1.0], [1, 1 / 3.0]]
      input_6 = [[1 / 3.0, 2 / 3.0], [1 / 3.0, 0], [1.0, 0]]
  
      expected = np.concatenate([input_1, input_3, input_4, input_6])
  
      return (
          input_sample,
          num_samples,
          problem,
          k_choices,
          groups,
          num_params,
          expected,
      )
  
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 a global function with example usage: setup_problem(setup_input) and returns: input_sample,  num_samples,  problem,  k_choices,  groups,  num_params,  expected