test get_latest_checkpoint_name() for legacy ckpts

tests/ml_tools/test_checkpointing.py

@@ -180,38 +180,24 @@ def loss_fn(model: QuantumModel, data: torch.Tensor) -> tuple[torch.Tensor, dict
         assert torch.allclose(loaded_model.expectation(inputs), model.expectation(inputs))
 
 
-def test_random_basicqQM_save_load_legacy_ckpts(
-    BasicQuantumModel: QuantumModel, tmp_path: Path
-) -> None:
+def test_basicqQM_save_load_legacy_ckpts(BasicQuantumModel: QuantumModel, tmp_path: Path) -> None:
     model = BasicQuantumModel
     optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
     ps0 = get_parameters(model)
     write_legacy_checkpoint(tmp_path, model, optimizer, 1)
-    loaded_model, optimizer, _ = load_checkpoint(
-        tmp_path,
-        model,
-        optimizer,
-        "model_QuantumModel_ckpt_001.pt",
-        "opt_Adam_ckpt_001.pt",
-    )
+    loaded_model, optimizer, _ = load_checkpoint(tmp_path, model, optimizer)
     ps1 = get_parameters(loaded_model)
     assert not torch.all(torch.isnan(loaded_model.expectation({})))
     assert torch.allclose(ps0, ps1)
 
 
-def test_random_basicqQNN_save_load_legacy_ckpts(BasicQNN: QNN, tmp_path: Path) -> None:
+def test_basicqQNN_save_load_legacy_ckpts(BasicQNN: QNN, tmp_path: Path) -> None:
     model = BasicQNN
     optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
     inputs = rand_featureparameters(model, 1)
     ps0 = get_parameters(model)
     write_legacy_checkpoint(tmp_path, model, optimizer, 1)
-    loaded_model, optimizer, _ = load_checkpoint(
-        tmp_path,
-        model,
-        optimizer,
-        "model_QNN_ckpt_001.pt",
-        "opt_Adam_ckpt_001.pt",
-    )
+    loaded_model, optimizer, _ = load_checkpoint(tmp_path, model, optimizer)
     ps1 = get_parameters(loaded_model)
     assert not torch.all(torch.isnan(model.expectation(inputs)))
     assert torch.allclose(ps0, ps1)