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docs/source-pytorch/common/checkpointing_intermediate.rst

@@ -23,9 +23,9 @@ For fine-grained control over checkpointing behavior, use the :class:`~lightning
         trainer.fit(model)
 
 .. note::
-    You can access the paths of saved checkpoints via the ``checkpoint_callback.best_model_path`` and 
-    ``checkpoint_callback.last_model_path`` properties or alternatively ``trainer.checkpoint_callback.best_model_path`` 
-    and ``trainer.checkpoint_callback.last_model_path``. These provide the file paths to the best checkpoint 
+    You can access the paths of saved checkpoints via the ``checkpoint_callback.best_model_path`` and
+    ``checkpoint_callback.last_model_path`` properties or alternatively ``trainer.checkpoint_callback.best_model_path``
+    and ``trainer.checkpoint_callback.last_model_path``. These provide the file paths to the best checkpoint
     (based on the monitored metric) and the most recently saved checkpoint, respectively.
 
 Any value that has been logged via *self.log* in the LightningModule can be monitored.