              update.loss = carry_over.loss + update.loss
              logger.debug("Merged carry over data: %s", update)
  
      def _process_data(self,
                        data: Dict[int, EventData],
                        is_live: bool) -> Tuple[List[float], List[List[float]]]:
          """ Process live update data.
  
          Live data requires different processing as often we will only have partial data for the
          current step, so we need to cache carried over partial data to be picked up at the next
          query. In addition to this, if training is unexpectedly interrupted, there may also be
 
</s> remove     def _collect_carry_over(self, data: Dict[int, EventData]) -> None:
 </s> add     def _collect_carry_over(self, data: dict[int, EventData]) -> None: </s> remove                   data: Dict[int, EventData],
                  is_live: bool) -> Tuple[np.ndarray, np.ndarray]:
 </s> add                   data: dict[int, EventData],
                  is_live: bool) -> tuple[np.ndarray, np.ndarray]: </s> remove                    data: Dict[int, EventData],
                   labels: List[str],
 </s> add                    data: dict[int, EventData],
                   labels: list[str], </s> remove         data: Dict[str, Any] = {}
 </s> add         data: dict[str, T.Any] = {} </s> remove         data: Dict[int, EventData] = {}
 </s> add         data: dict[int, EventData] = {}
                      data: dict[int, EventData],
                      is_live: bool) -> tuple[list[float], list[list[float]]]:
              update.loss = carry_over.loss + update.loss
              logger.debug("Merged carry over data: %s", update)
  
      def _process_data(self,
                      data: dict[int, EventData],
                      is_live: bool) -> tuple[list[float], list[list[float]]]:
                      data: dict[int, EventData],
                      is_live: bool) -> tuple[list[float], list[list[float]]]:
          """ Process live update data.
  
          Live data requires different processing as often we will only have partial data for the
          current step, so we need to cache carried over partial data to be picked up at the next
          query. In addition to this, if training is unexpectedly interrupted, there may also be
keep keep keep keep replace replace keep keep keep keep keep
 Rebase code (#1326)

* Remove tensorflow_probability requirement

* setup.py - fix progress bars

* requirements.txt: Remove pre python 3.9 packages

* update apple requirements.txt

* update INSTALL.md

* Remove python<3.9 code

* setup.py - fix Windows Installer

* typing: python3.9 compliant

* Update pytest and readthedocs python versions

* typing fixes

* Python Version updates
  - Reduce max version to 3.10
  - Default to 3.10 in installers
  - Remove incompatible 3.11 tests

* Update dependencies

* Downgrade imageio dep for Windows

* typing: merge optional unions and fixes

* Updates
  - min python version 3.10
  - typing to python 3.10 spec
  - remove pre-tf2.10 code
  - Add conda tests

* train: re-enable optimizer saving

* Update dockerfiles

* Update setup.py
  - Apple Conda deps to setup.py
  - Better Cuda + dependency handling

* bugfix: Patch logging to prevent Autograph errors

* Update dockerfiles

* Setup.py - Setup.py - stdout to utf-8

* Add more OSes to github Actions

* suppress mac-os end to end test 
lib/gui/analysis/event_reader.py