Smaller docfix for cli scheduler documentation

docs/source-pytorch/cli/lightning_cli_intermediate_2.rst

@@ -201,9 +201,10 @@ If the scheduler you want needs other arguments, add them via the CLI (no need t
 
 .. code:: bash
 
-    python main.py fit --optimizer=Adam --lr_scheduler=ReduceLROnPlateau --lr_scheduler.monitor=epoch
+    python main.py fit --optimizer=Adam --lr_scheduler=ReduceLROnPlateau --lr_scheduler.monitor=train_loss
 
-Furthermore, any custom subclass of ``torch.optim.lr_scheduler.LRScheduler`` can be used as learning rate scheduler:
+(assuming you have a ``train_loss`` metric logged). Furthermore, any custom subclass of
+``torch.optim.lr_scheduler.LRScheduler`` can be used as learning rate scheduler:
 
 .. code:: python
 
@@ -212,7 +213,6 @@ Furthermore, any custom subclass of ``torch.optim.lr_scheduler.LRScheduler`` can
     from lightning.pytorch.cli import LightningCLI
     from lightning.pytorch.demos.boring_classes import DemoModel, BoringDataModule
 
-
     class LitLRScheduler(torch.optim.lr_scheduler.CosineAnnealingLR):
         def step(self):
             print("⚡", "using LitLRScheduler", "⚡")

src/lightning/pytorch/cli.py

@@ -66,6 +66,13 @@
 
 
 class ReduceLROnPlateau(torch.optim.lr_scheduler.ReduceLROnPlateau):
+    """Custom ReduceLROnPlateau scheduler that extends PyTorch's ReduceLROnPlateau.
+
+    This class adds a `monitor` attribute to the standard PyTorch ReduceLROnPlateau to specify which metric should be
+    tracked for learning rate adjustment.
+
+    """
+
     def __init__(self, optimizer: Optimizer, monitor: str, *args: Any, **kwargs: Any) -> None:
         super().__init__(optimizer, *args, **kwargs)
         self.monitor = monitor