Add back test

tests/tests_pytorch/test_cli.py

@@ -517,6 +517,41 @@ def __init__(self, submodule1: LightningModule, submodule2: LightningModule, mai
     assert isinstance(cli.model.submodule2, BoringModel)
 
 
+def test_lightning_cli_torch_modules(tmpdir):
+    class TestModule(BoringModel):
+        def __init__(self, activation: torch.nn.Module = None, transform: Optional[List[torch.nn.Module]] = None):
+            super().__init__()
+            self.activation = activation
+            self.transform = transform
+
+    config = """model:
+        activation:
+          class_path: torch.nn.LeakyReLU
+          init_args:
+            negative_slope: 0.2
+        transform:
+          - class_path: torchvision.transforms.Resize
+            init_args:
+              size: 64
+          - class_path: torchvision.transforms.CenterCrop
+            init_args:
+              size: 64
+    """
+    config_path = tmpdir / "config.yaml"
+    with open(config_path, "w") as f:
+        f.write(config)
+
+    cli_args = [f"--trainer.default_root_dir={tmpdir}", f"--config={str(config_path)}"]
+
+    with mock.patch("sys.argv", ["any.py"] + cli_args):
+        cli = LightningCLI(TestModule, run=False)
+
+    assert isinstance(cli.model.activation, torch.nn.LeakyReLU)
+    assert cli.model.activation.negative_slope == 0.2
+    assert len(cli.model.transform) == 2
+    assert all(isinstance(v, torch.nn.Module) for v in cli.model.transform)
+
+
 class BoringModelRequiredClasses(BoringModel):
     def __init__(self, num_classes: int, batch_size: int = 8):
         super().__init__()