test convolutions can be loaded properly

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

 from mlagents.trainers.model_saver.torch_model_saver import TorchModelSaver
 from mlagents.trainers.settings import (
     TrainerSettings,
+    NetworkSettings,
+    EncoderType,
     PPOSettings,
     SACSettings,
     POCASettings,
     _compare_two_policies(policy2, policy3)
     # Assert that the steps are 0.
     assert policy3.get_current_step() == 0
+
+
+@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
+def test_load_policy_different_hidden_units(tmp_path, vis_encode_type):
+    path1 = os.path.join(tmp_path, "runid1")
+    trainer_params = TrainerSettings()
+    trainer_params.network_settings = NetworkSettings(
+        hidden_units=12, vis_encode_type=EncoderType(vis_encode_type)
+    )
+    policy = create_policy_mock(trainer_params, use_visual=True)
+    conv_params = [mod for mod in policy.actor.parameters() if len(mod.shape) > 2]
+
+    model_saver = TorchModelSaver(trainer_params, path1)
+    model_saver.register(policy)
+    model_saver.initialize_or_load(policy)
+    policy.set_step(2000)
+
+    mock_brain_name = "MockBrain"
+    model_saver.save_checkpoint(mock_brain_name, 2000)
+
+    # Try load from this path
+    trainer_params2 = TrainerSettings()
+    trainer_params2.network_settings = NetworkSettings(
+        hidden_units=10, vis_encode_type=EncoderType(vis_encode_type)
+    )
+    model_saver2 = TorchModelSaver(trainer_params2, path1, load=True)
+    policy2 = create_policy_mock(trainer_params2, use_visual=True)
+    conv_params2 = [mod for mod in policy2.actor.parameters() if len(mod.shape) > 2]
+    # asserts convolutions have different parameters before load
+    for conv1, conv2 in zip(conv_params, conv_params2):
+        assert not torch.equal(conv1, conv2)
+    model_saver2.register(policy2)
+    model_saver2.initialize_or_load(policy2)
+    # asserts convolutions have same parameters after load
+    for conv1, conv2 in zip(conv_params, conv_params2):
+        assert torch.equal(conv1, conv2)
 
 
 @pytest.mark.parametrize(