add note and fix docstring

Author: SkafteNicki

docs/source-pytorch/common/checkpointing_intermediate.rst

@@ -21,7 +21,12 @@ For fine-grained control over checkpointing behavior, use the :class:`~lightning
         checkpoint_callback = ModelCheckpoint(dirpath="my/path/", save_top_k=2, monitor="val_loss")
         trainer = Trainer(callbacks=[checkpoint_callback])
         trainer.fit(model)
-        checkpoint_callback.best_model_path
+
+.. 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 
+    (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.
 

src/lightning/pytorch/callbacks/model_checkpoint.py

@@ -204,11 +204,11 @@ class ModelCheckpoint(Checkpoint):
         ... )
 
         # retrieve the best checkpoint after training
-        checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
-        trainer = Trainer(callbacks=[checkpoint_callback])
-        model = ...
-        trainer.fit(model)
-        checkpoint_callback.best_model_path
+        >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
+        >>> trainer = Trainer(callbacks=[checkpoint_callback])
+        >>> model = ...  # doctest: +SKIP
+        >>> trainer.fit(model)  # doctest: +SKIP
+        >>> print(checkpoint_callback.best_model_path)  # doctest: +SKIP
 
     .. tip:: Saving and restoring multiple checkpoint callbacks at the same time is supported under variation in the
         following arguments: