small improvement

ml-agents/mlagents/trainers/ppo/trainer.py

             self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
 
         if self.saver is None:
-            self.saver = self.create_saver(policy=policy)
+            self.saver = self.create_saver(
+                self.framework,
+                policy,
+                self.trainer_settings,
+                self.artifact_path,
+                self.load,
+            )
             self.saver.register(self.policy)
             self.saver.register(self.optimizer)
             self.saver.maybe_load()

ml-agents/mlagents/trainers/sac/trainer.py

             self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
 
         if self.saver is None:
-            self.saver = self.create_saver(policy=policy)
+            self.saver = self.create_saver(
+                self.framework,
+                policy,
+                self.trainer_settings,
+                self.artifact_path,
+                self.load,
+            )
             self.saver.register(self.policy)
             self.saver.register(self.optimizer)
             self.saver.maybe_load()

ml-agents/mlagents/trainers/torch/model_serialization.py

         }
         self.dummy_input = (dummy_vec_obs, dummy_vis_obs, dummy_masks, dummy_memories)
 
-    @staticmethod
     def export_policy_model(self, output_filepath: str) -> None:
         """
         Exports a Torch model for a Policy to .onnx format for Unity embedding.

ml-agents/mlagents/trainers/trainer/rl_trainer.py

 from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
 from mlagents.trainers.agent_processor import AgentManagerQueue
 from mlagents.trainers.trajectory import Trajectory
-from mlagents.trainers.settings import TestingConfiguration
+from mlagents.trainers.settings import TestingConfiguration, TrainerSettings
 from mlagents.trainers.stats import StatsPropertyType
 from mlagents.trainers.saver.saver import BaseSaver
 from mlagents.trainers.saver.torch_saver import TorchSaver
         pass
 
     @staticmethod
-    def create_saver(self, policy: Policy) -> BaseSaver:
-        if self.framework == "torch":
+    def create_saver(
+        framework: str,
+        policy: Policy,
+        trainer_settings: TrainerSettings,
+        model_path: str,
+        load: bool,
+    ) -> BaseSaver:
+        if framework == "torch":
-                self.trainer_settings,
-                model_path=self.artifact_path,
-                load=self.load,
+                trainer_settings,
+                model_path,
+                load,
-                self.trainer_settings,
-                model_path=self.artifact_path,
-                load=self.load,
+                trainer_settings,
+                model_path,
+                load,
             )
         return saver