diff --git a/docs/source/checkpointing.rst b/docs/source/checkpointing.rst
index 6f7c9c0ef8..9596c0d776 100644
--- a/docs/source/checkpointing.rst
+++ b/docs/source/checkpointing.rst
@@ -119,6 +119,7 @@ By specifying the monitored metric to be "train_loss", the checkpointer will exp
 Later on, the best checkpoint can be loaded via
 
 .. code-block:: python
+
     TorchSnapshotSaver.restore_from_best(your_dirpath_here, unit, metric_name="train_loss", mode="min")
 
 If you'd like to monitor a validation metric (say validation loss after each eval epoch during :py:func:`~torchtnt.framework.fit.fit`), you can use the `save_every_n_eval_epochs` flag instead, like so