code: def test_run_basic_search():
      def objective(args):
          case, val = args
          if case == "case 1":
              return val
          else:
              return val**2
  
      # define a search space
      space = hp.choice(
          "a",
          [
              ("case 1", 1 + hp.lognormal("c1", 0, 1)),
              ("case 2", hp.uniform("c2", -10, 10)),
          ],
      )
  
      # minimize the objective over the space
      # NOTE: Max evals should be greater than 10, as the first 10 runs are only the initialization rounds
      best = fmin(
          objective,
          space,
          algo=atpe.suggest,
          max_evals=20,
          rstate=np.random.default_rng(1),
      )
  
      assert best["a"] == 1
      assert space_eval(space, best)[0] == "case 2"
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_run_basic_search() and returns: val, unknown