Corrected all training data deletion function documentation in user g…

…uides

docs/user_guides/fs/feature_view/training-data.md

@@ -101,7 +101,7 @@ To clean up unused training data, you can delete all training data or for a part
 feature_view.delete_training_dataset(version=1)
 
 # delete all training datasets
-feature_view.delete_training_dataset()
+feature_view.delete_all_training_datasets()
 ```
 It is also possible to keep the metadata and delete only the materialised files. Then you can recreate the deleted files by just specifying a version, and you get back the exact same dataset again. This is useful when you are running out of storage.
 ```python