code: def testConvertOptuna(self):
      from ray.tune.search.optuna import OptunaSearch
      import optuna
      from optuna.samplers import RandomSampler
  
      # Grid search not supported, should raise ValueError
      with self.assertRaises(ValueError):
          OptunaSearch.convert_search_space(
              {"grid": tune.grid_search([0, 1])}
          )
  
      config = {
          "a": ray.tune.search.sample.Categorical([2, 3, 4]).uniform(),
          "b": {
              "x": ray.tune.search.sample.Integer(0, 5).quantized(2),
              "y": 4,
              "z": ray.tune.search.sample.Float(
                  1e-4, 1e-2
              ).loguniform(),
          },
      }
      converted_config = OptunaSearch.convert_search_space(config)
      optuna_config = {
          "a": optuna.distributions.CategoricalDistribution([2, 3, 4]),
          "b": {
              "x": optuna.distributions.IntDistribution(0, 5, step=2),
              "z": optuna.distributions.FloatDistribution(
                  1e-4, 1e-2, log=True
              ),
          },
      }
  
      def optuna_define_by_run(ot_trial):
          ot_trial.suggest_categorical("a", [2, 3, 4])
          ot_trial.suggest_int("b/x", 0, 5, 2)
          ot_trial.suggest_loguniform("b/z", 1e-4, 1e-2)
  
      def optuna_define_by_run_with_constants(ot_trial):
          ot_trial.suggest_categorical("a", [2, 3, 4])
          ot_trial.suggest_int("b/x", 0, 5, 2)
          ot_trial.suggest_loguniform("b/z", 1e-4, 1e-2)
          return {"constant": 1}
  
      def optuna_define_by_run_invalid(ot_trial):
          ot_trial.suggest_categorical("a", [2, 3, 4])
          ot_trial.suggest_int("b/x", 0, 5, 2)
          ot_trial.suggest_loguniform("b/z", 1e-4, 1e-2)
          return 1
  
      sampler1 = RandomSampler(seed=1234)
      searcher1 = OptunaSearch(
          space=converted_config,
          sampler=sampler1,
          metric="a",
          mode="max",
      )
  
      sampler2 = RandomSampler(seed=1234)
      searcher2 = OptunaSearch(
          space=optuna_config, sampler=sampler2, metric="a", mode="max"
      )
  
      sampler3 = RandomSampler(seed=1234)
      searcher3 = OptunaSearch(
          space=optuna_define_by_run,
          sampler=sampler3,
          metric="a",
          mode="max",
      )
  
      sampler4 = RandomSampler(seed=1234)
      searcher4 = OptunaSearch(
          space=optuna_define_by_run_with_constants,
          sampler=sampler4,
          metric="a",
          mode="max",
      )
  
      config_constant = searcher4.suggest("0")
      self.assertIn("constant", config_constant)
      config_constant.pop("constant")
  
      sampler5 = RandomSampler(seed=1234)
      searcher5 = OptunaSearch(
          space=optuna_define_by_run_invalid,
          sampler=sampler5,
          metric="a",
          mode="max",
      )
  
      with self.assertRaises(TypeError):
          searcher5.suggest("0")
  
      config1 = searcher1.suggest("0")
      config2 = searcher2.suggest("0")
      config3 = searcher3.suggest("0")
  
      self.assertEqual(config1, config2)
      self.assertEqual(config1, config3)
      self.assertEqual(config1, config_constant)
      self.assertIn(config1["a"], [2, 3, 4])
      self.assertIn(config1["b"]["x"], list(range(5)))
      self.assertLess(1e-4, config1["b"]["z"])
      self.assertLess(config1["b"]["z"], 1e-2)
  
      def optuna_define_by_run_branching_invalid(ot_trial):
          # this is invalid because such a dict cannot be
          # unflattened (will try to assign child dicts to value under "a",
          # but that will be an int, instead of a dict)
          a = ot_trial.suggest_categorical("a", [1, 2])
          if a == 1:
              ot_trial.suggest_int("a/b", 0, 3)
              ot_trial.suggest_int("a/first", 2, 8)
          else:
              ot_trial.suggest_int("a/b", 4, 10)
              ot_trial.suggest_uniform("a/second", -0.4, 0.4)
  
      def optuna_define_by_run_branching(ot_trial):
          a = ot_trial.suggest_categorical("a", ["1", "2"])
          if a == "1":
              ot_trial.suggest_int("nest/b", 0, 3)
              ot_trial.suggest_int("nest/first", 2, 8)
          else:
              ot_trial.suggest_int("nest/b", 4, 10)
              ot_trial.suggest_uniform("nest/second", -0.4, 0.4)
  
      class MockOptunaSampler(RandomSampler):
          def __init__(self, seed) -> None:
              super().__init__(seed=seed)
              self.counter = 0
  
          def sample_independent(
              self, study, trial, param_name, param_distribution
          ):
              if param_name == "a":
                  if self.counter == 0:
                      self.counter += 1
                      return param_distribution.choices[0]
                  return param_distribution.choices[1]
              return super().sample_independent(
                  study, trial, param_name, param_distribution
              )
  
      sampler_branching = RandomSampler(seed=1234)
      searcher_branching = OptunaSearch(
          space=optuna_define_by_run_branching_invalid,
          sampler=sampler_branching,
          metric="a",
          mode="max",
      )
  
      with self.assertRaises(TypeError):
          searcher_branching.suggest("0")
  
      sampler_branching = MockOptunaSampler(seed=1234)
      searcher_branching = OptunaSearch(
          space=optuna_define_by_run_branching,
          sampler=sampler_branching,
          metric="a",
          mode="max",
      )
  
      config_branching_1 = searcher_branching.suggest("0")
      self.assertIn("a", config_branching_1)
      self.assertEqual(config_branching_1["a"], "1")
      self.assertIn("nest", config_branching_1)
      self.assertIn("b", config_branching_1["nest"])
      self.assertIn("first", config_branching_1["nest"])
      self.assertGreater(4, config_branching_1["nest"]["b"])
      self.assertLess(0.5, config_branching_1["nest"]["first"])
  
      config_branching_2 = searcher_branching.suggest("1")
      self.assertIn("a", config_branching_2)
      self.assertEqual(config_branching_2["a"], "2")
      self.assertIn("nest", config_branching_2)
      self.assertIn("b", config_branching_2["nest"])
      self.assertIn("second", config_branching_2["nest"])
      self.assertLess(3, config_branching_2["nest"]["b"])
      self.assertGreater(0.5, config_branching_2["nest"]["second"])
  
      searcher = OptunaSearch(metric="a", mode="max")
      analysis = tune.run(
          _mock_objective,
          config=config,
          search_alg=searcher,
          num_samples=1,
      )
      trial = analysis.trials[0]
      assert trial.config["a"] in [2, 3, 4]
  
      mixed_config = {
          "a": tune.uniform(5, 6),
          "b": tune.uniform(8, 9),  # Cannot mix List and Dict
      }
      searcher = OptunaSearch(
          space=mixed_config, metric="a", mode="max"
      )
      config = searcher.suggest("0")
      self.assertTrue(5 <= config["a"] <= 6)
      self.assertTrue(8 <= config["b"] <= 9)
  
imports: from collections import defaultdict
  from unittest.mock import patch
  import numpy
  import unittest
  import ray
  import ray.tune.search.sample
  from ray import train, tune
  from ray.tune import Experiment
  from ray.tune.search.util import logger
  from ray.tune.search.variant_generator import generate_variants
label_desc: Use image node_id 29 for calling the SearchSpaceTest obj's underlying member method code with example usage: obj.testConvertOptuna() and returns: dict, int, super, param_distribution, param_distribution