update test_saver_reward_providers.py

ml-agents/mlagents/trainers/tests/torch/saver/test_saver_reward_providers.py

 
 from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
 from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
-from mlagents.trainers.saver.torch_saver import TorchSaver
+from mlagents.trainers.model_saver.torch_model_saver import TorchModelSaver
 from mlagents.trainers.settings import (
     TrainerSettings,
     RewardSignalType,
 
     # save at path 1
     path1 = os.path.join(tmp_path, "runid1")
-    saver = TorchSaver(trainer_settings, path1)
-    saver.register(policy)
-    saver.register(optimizer)
-    saver.initialize_or_load()
+    model_saver = TorchModelSaver(trainer_settings, path1)
+    model_saver.register(policy)
+    model_saver.register(optimizer)
+    model_saver.initialize_or_load()
-    saver.save_checkpoint("MockBrain", 2000)
+    model_saver.save_checkpoint("MockBrain", 2000)
 
     # create a new optimizer and policy
     optimizer2 = OptimizerClass(policy, trainer_settings)
-    saver2 = TorchSaver(trainer_settings, path1, load=True)
-    saver2.register(policy2)
-    saver2.register(optimizer2)
-    saver2.initialize_or_load()  # This is to load the optimizers
+    model_saver2 = TorchModelSaver(trainer_settings, path1, load=True)
+    model_saver2.register(policy2)
+    model_saver2.register(optimizer2)
+    model_saver2.initialize_or_load()  # This is to load the optimizers
 
     # assert the models have the same weights
     module_dict_1 = optimizer.get_modules()