diff --git a/src/lightning/pytorch/core/module.py b/src/lightning/pytorch/core/module.py
index 85f631ee40f75..78d805ef5074f 100644
--- a/src/lightning/pytorch/core/module.py
+++ b/src/lightning/pytorch/core/module.py
@@ -694,18 +694,17 @@ def all_gather(
         return apply_to_collection(data, Tensor, all_gather, group=group, sync_grads=sync_grads)
 
     @override
-    def forward(self, *args: Any, **kwargs: Any) -> Any:
+    def forward(self, *inputs) -> Any:
         r"""Same as :meth:`torch.nn.Module.forward`.
 
         Args:
             *args: Whatever you decide to pass into the forward method.
             **kwargs: Keyword arguments are also possible.
-
         Return:
             Your model's output
 
         """
-        return super().forward(*args, **kwargs)
+        return super().forward(*inputs)
 
     def training_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
         r"""Here you compute and return the training loss and some additional metrics for e.g. the progress bar or
diff --git a/tests/tests_pytorch/models/test_torchscript.py b/tests/tests_pytorch/models/test_torchscript.py
index 29f251044c0b5..781656d61b7ae 100644
--- a/tests/tests_pytorch/models/test_torchscript.py
+++ b/tests/tests_pytorch/models/test_torchscript.py
@@ -27,6 +27,14 @@
 from tests_pytorch.helpers.runif import RunIf
 
 
+def test_torchscript_vanilla():
+    """Test that LightningModule itself can be converted."""
+    model = LightningModule()
+
+    script = model.to_torchscript()
+    assert isinstance(script, torch.jit.ScriptModule)
+
+
 @pytest.mark.skipif(_IS_WINDOWS and _TORCH_GREATER_EQUAL_2_4, reason="not close on Windows + PyTorch 2.4")
 @pytest.mark.parametrize("modelclass", [BoringModel, ParityModuleRNN, BasicGAN])
 def test_torchscript_input_output(modelclass):