code: def test_study_optimize_with_single_search_space() -> None:
      def objective(trial: Trial) -> float:
          a = trial.suggest_int("a", 0, 2)
  
          if a == 0:
              b = trial.suggest_float("b", -1.0, 1.0, step=0.5)
              return a + b
          elif a == 1:
              c = trial.suggest_categorical("c", ["x", "y", None])
              if c == "x":
                  return a + 1
              else:
                  return a - 1
          else:
              return a * 2
  
      study = optuna.create_study(sampler=samplers.BruteForceSampler())
      study.optimize(objective)
  
      expected_suggested_values = [
          {"a": 0, "b": -1.0},
          {"a": 0, "b": -0.5},
          {"a": 0, "b": 0.0},
          {"a": 0, "b": 0.5},
          {"a": 0, "b": 1.0},
          {"a": 1, "c": "x"},
          {"a": 1, "c": "y"},
          {"a": 1, "c": None},
          {"a": 2},
      ]
      all_suggested_values = [t.params for t in study.trials]
      assert len(all_suggested_values) == len(expected_suggested_values)
      for a in all_suggested_values:
          assert a in expected_suggested_values
  
file_path: optuna/tests/samplers_tests/test_brute_force.py
imports: import numpy
  import pytest
  import optuna
  from optuna import samplers
  from optuna.samplers._brute_force import _TreeNode
  from optuna.trial import Trial
label_desc: Use image node_id 4 for calling a global function with example usage: test_study_optimize_with_single_search_space() and returns: unknown, unknown, unknown, unknown