Reformat using black

ml-agents/mlagents/trainers/components/reward_signals/gail/signal.py

         super().check_config(config_dict, param_keys)
 
     def prepare_update(
-        self,
-        policy: TFPolicy,
-        mini_batch: Dict[str, np.ndarray],
-        num_sequences: int,
+        self, policy: TFPolicy, mini_batch: Dict[str, np.ndarray], num_sequences: int
     ) -> Dict[tf.Tensor, Any]:
         """
         Prepare inputs for update. .

ml-agents/mlagents/trainers/components/reward_signals/reward_signal_factory.py

 
 
 def create_reward_signal(
-    policy: TFPolicy,
-    name: str,
-    config_entry: Dict[str, Any],
+    policy: TFPolicy, name: str, config_entry: Dict[str, Any]
 ) -> RewardSignal:
     """
     Creates a reward signal class based on the name and config entry provided as a dict.