Use SimpleBaseSampler instead of SimpleSampler in tutorial

package/samplers/simple/__init__.py

@@ -81,7 +81,3 @@ def _default_sample_independent(
         return self._random_sampler.sample_independent(
             study, trial, param_name, param_distribution
         )
-
-
-class SimpleSampler(SimpleBaseSampler):
-    pass

recipes/001_first.py

@@ -43,13 +43,15 @@
 # `force_reload=True` argument forces downloading the sampler from the registry.
 # If we set `force_reload` to `False`, we use the cached data in our local storage if available.
 
-SimpleSampler = optunahub.load_module("samplers/simple").SimpleSampler
+SimpleBaseSampler = optunahub.load_module("samplers/simple").SimpleBaseSampler
 
 
-class MySampler(SimpleSampler):  # type: ignore
+class MySampler(SimpleBaseSampler):  # type: ignore
     # By default, search space will be estimated automatically like Optuna's built-in samplers.
     # You can fix the search spacd by `search_space` argument of `SimpleSampler` class.
-    def __init__(self, search_space: dict[str, optuna.distributions.BaseDistribution]) -> None:
+    def __init__(
+        self, search_space: dict[str, optuna.distributions.BaseDistribution] | None = None
+    ) -> None:
         super().__init__(search_space)
         self._rng = np.random.RandomState()
 
@@ -67,6 +69,10 @@ def sample_relative(
         # `search_space` argument must be identical to `search_space` argument input to `__init__` method.
         # This method is automatically invoked by Optuna and `SimpleBaseSampler`.
 
+        # If search space is empty, all parameter values are sampled randomly by SimpleBaseSampler.
+        if search_space == {}:
+            return {}
+
         params = {}  # type: dict[str, Any]
         for n, d in search_space.items():
             if isinstance(d, optuna.distributions.FloatDistribution):
@@ -92,7 +98,7 @@ def objective(trial: optuna.trial.Trial) -> float:
 ###################################################################################################
 # This sampler can be used in the same way as other Optuna samplers.
 # In the following example, we create a study and optimize it using `MySampler` class.
-sampler = MySampler({"x": optuna.distributions.FloatDistribution(-10, 10)})
+sampler = MySampler()
 study = optuna.create_study(sampler=sampler)
 study.optimize(objective, n_trials=100)