Loop flattening: remove .replace()

docs/source-pytorch/extensions/loops.rst

@@ -262,16 +262,7 @@ run (optional)
 Subloops
 --------
 
-When you want to customize nested loops within loops, use the :meth:`~pytorch_lightning.loops.loop.Loop.replace` method:
-
-.. code-block:: python
-
-    # This takes care of properly instantiating the new Loop and setting all references
-    trainer.fit_loop.replace(epoch_loop=MyEpochLoop)
-    # Trainer runs the fit loop with your new epoch loop!
-    trainer.fit(model)
-
-Alternatively, for more fine-grained control, use the :meth:`~pytorch_lightning.loops.loop.Loop.connect` method:
+When you want to customize nested loops within loops use the :meth:`~pytorch_lightning.loops.loop.Loop.connect` method:
 
 .. code-block:: python
 

examples/README.md

@@ -35,12 +35,3 @@ ______________________________________________________________________
 This folder contains older examples. You should instead use the examples
 in [Lightning Bolts](https://pytorch-lightning.readthedocs.io/en/latest/ecosystem/bolts.html)
 for advanced use cases.
-
-______________________________________________________________________
-
-## Loop examples
-
-Contains implementations leveraging [loop customization](https://pytorch-lightning.readthedocs.io/en/latest/extensions/loops.html) to enhance the Trainer with new optimization routines.
-
-- [K-fold Cross Validation Loop](pl_loops/kfold.py): Implementation of cross validation in a loop and special datamodule.
-- [Yield Loop](pl_loops/yielding_training_step.py): Enables yielding from the training_step like in a Python generator. Useful for automatic optimization with multiple optimizers.