diff --git a/obp/dataset/synthetic.py b/obp/dataset/synthetic.py
index 83d9620c..50488fff 100644
--- a/obp/dataset/synthetic.py
+++ b/obp/dataset/synthetic.py
@@ -201,7 +201,8 @@ def obtain_batch_bandit_feedback(self, n_rounds: int) -> BanditFeedback:
             action = np.array(
                 [
                     self.random_.choice(
-                        np.arange(self.n_actions), p=behavior_policy_[i],
+                        np.arange(self.n_actions),
+                        p=behavior_policy_[i],
                     )
                     for i in np.arange(n_rounds)
                 ]
@@ -289,7 +290,9 @@ def calc_ground_truth_policy_value(
 
 
 def logistic_reward_function(
-    context: np.ndarray, action_context: np.ndarray, random_state: Optional[int] = None,
+    context: np.ndarray,
+    action_context: np.ndarray,
+    random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Logistic mean reward function for synthetic bandit datasets.
 
@@ -328,7 +331,9 @@ def logistic_reward_function(
 
 
 def linear_reward_function(
-    context: np.ndarray, action_context: np.ndarray, random_state: Optional[int] = None,
+    context: np.ndarray,
+    action_context: np.ndarray,
+    random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Linear mean reward function for synthetic bandit datasets.
 
@@ -367,7 +372,9 @@ def linear_reward_function(
 
 
 def linear_behavior_policy(
-    context: np.ndarray, action_context: np.ndarray, random_state: Optional[int] = None,
+    context: np.ndarray,
+    action_context: np.ndarray,
+    random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Linear contextual behavior policy for synthetic bandit datasets.
 
diff --git a/tests/dataset/test_synthetic.py b/tests/dataset/test_synthetic.py
index a495723a..d55d269b 100644
--- a/tests/dataset/test_synthetic.py
+++ b/tests/dataset/test_synthetic.py
@@ -110,15 +110,22 @@ def test_synthetic_obtain_batch_bandit_feedback():
 ]
 
 valid_input_of_calc_policy_value = [
-    (np.ones((2, 3)), np.ones((2, 3, 1)), "valid shape",),
+    (
+        np.ones((2, 3)),
+        np.ones((2, 3, 1)),
+        "valid shape",
+    ),
 ]
 
 
 @pytest.mark.parametrize(
-    "expected_reward, action_dist, description", invalid_input_of_calc_policy_value,
+    "expected_reward, action_dist, description",
+    invalid_input_of_calc_policy_value,
 )
 def test_synthetic_calc_policy_value_using_invalid_inputs(
-    expected_reward, action_dist, description,
+    expected_reward,
+    action_dist,
+    description,
 ):
     n_actions = 10
     dataset = SyntheticBanditDataset(n_actions=n_actions)
@@ -130,10 +137,13 @@ def test_synthetic_calc_policy_value_using_invalid_inputs(
 
 
 @pytest.mark.parametrize(
-    "expected_reward, action_dist, description", valid_input_of_calc_policy_value,
+    "expected_reward, action_dist, description",
+    valid_input_of_calc_policy_value,
 )
 def test_synthetic_calc_policy_value_using_valid_inputs(
-    expected_reward, action_dist, description,
+    expected_reward,
+    action_dist,
+    description,
 ):
     n_actions = 10
     dataset = SyntheticBanditDataset(n_actions=n_actions)