[tune] hyperopt searcher to support tune.choice([[1,2],[3,4]]). (#24181)

Have Hyperopt Searcher to support tune.choice([1,2],[3,4]) type search space.

python/ray/tune/suggest/hyperopt.py

@@ -472,6 +472,8 @@ def resolve_value(par: str, domain: Domain) -> Any:
                                 dict(enumerate(category)), prefix=f"{par}/{i}"
                             )
                             if isinstance(category, list)
+                            and len(category) > 0
+                            and isinstance(category[0], Domain)
                             else resolve_value(f"{par}/{i}", category)
                             if isinstance(category, Domain)
                             else category

python/ray/tune/tests/test_sample.py

@@ -982,6 +982,38 @@ def testConvertHyperOpt(self):
         self.assertTrue(5 <= config["a"] <= 6)
         self.assertTrue(8 <= config["b"] <= 9)
 
+    def testConvertHyperOptChooseFromListOfList(self):
+        from ray.tune.suggest.hyperopt import HyperOptSearch
+        from hyperopt import hp
+
+        config = {
+            "a": tune.choice([[1, 2], [3, 4]]),
+        }
+        converted_config = HyperOptSearch.convert_search_space(config)
+        hyperopt_config = {
+            "a": hp.choice("a", [[1, 2], [3, 4]]),
+        }
+
+        searcher1 = HyperOptSearch(
+            space=converted_config, random_state_seed=1234, metric="a", mode="max"
+        )
+        searcher2 = HyperOptSearch(
+            space=hyperopt_config, random_state_seed=1234, metric="a", mode="max"
+        )
+
+        config1 = searcher1.suggest("0")
+        config2 = searcher2.suggest("0")
+
+        self.assertEqual(config1, config2)
+
+        # Hyperopt natively converts list to tuple.
+        # Try out the following script:
+        # ```
+        # a = HyperOptSearch.convert_search_space({"a": tune.choice([[1,2], [3,4]])})
+        # print(hyperopt.pyll.stochastic.sample(a))
+        # ```
+        self.assertTrue(config1.get("a") in [(1, 2), (3, 4)])
+
     def testConvertHyperOptNested(self):
         from ray.tune.suggest.hyperopt import HyperOptSearch