update torch saver test

ml-agents/mlagents/trainers/tests/torch/test_saver.py

 import torch
 from mlagents.trainers.policy.torch_policy import TorchPolicy
 from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
-from mlagents.trainers.saver.torch_saver import TorchSaver
+from mlagents.trainers.model_saver.torch_model_saver import TorchModelSaver
 from mlagents.trainers.settings import TrainerSettings
 from mlagents.trainers.tests import mock_brain as mb
 from mlagents.trainers.tests.torch.test_policy import create_policy_mock
     trainer_params = TrainerSettings()
-    saver = TorchSaver(trainer_params, tmp_path)
+    model_saver = TorchModelSaver(trainer_params, tmp_path)
-    saver.register(opt)
-    assert saver.policy is None
+    model_saver.register(opt)
+    assert model_saver.policy is None
-    saver.register(policy)
-    assert saver.policy is not None
+    model_saver.register(policy)
+    assert model_saver.policy is not None
 
 
 def test_load_save(tmp_path):
     policy = create_policy_mock(trainer_params)
-    saver = TorchSaver(trainer_params, path1)
-    saver.register(policy)
-    saver.initialize_or_load(policy)
+    model_saver = TorchModelSaver(trainer_params, path1)
+    model_saver.register(policy)
+    model_saver.initialize_or_load(policy)
-    saver.save_checkpoint(mock_brain_name, 2000)
+    model_saver.save_checkpoint(mock_brain_name, 2000)
-    saver2 = TorchSaver(trainer_params, path1, load=True)
+    model_saver2 = TorchModelSaver(trainer_params, path1, load=True)
-    saver2.register(policy2)
-    saver2.initialize_or_load(policy2)
+    model_saver2.register(policy2)
+    model_saver2.initialize_or_load(policy2)
-    saver3 = TorchSaver(trainer_params, path2)
+    model_saver3 = TorchModelSaver(trainer_params, path2)
-    saver3.register(policy3)
-    saver3.initialize_or_load(policy3)
+    model_saver3.register(policy3)
+    model_saver3.initialize_or_load(policy3)
     _compare_two_policies(policy2, policy3)
     # Assert that the steps are 0.
     assert policy3.get_current_step() == 0
         dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
     )
     trainer_params = TrainerSettings()
-    saver = TorchSaver(trainer_params, model_path)
-    saver.register(policy)
-    saver.save_checkpoint("Mock_Brain", 100)
+    model_saver = TorchModelSaver(trainer_params, model_path)
+    model_saver.register(policy)
+    model_saver.save_checkpoint("Mock_Brain", 100)
     assert os.path.isfile(model_path + "/Mock_Brain-100.onnx")