          if do_snapshot:
              self._model.snapshot()
          self._update_viewers(viewer, timelapse_kwargs)
  
      def _log_tensorboard(self, loss: List[float]) -> None:
          """ Log current loss to Tensorboard log files
  
          Parameters
          ----------
          loss: list
 
</s> remove     def _print_loss(self, loss: List[float]) -> None:
 </s> add     def _print_loss(self, loss: T.List[float]) -> None: </s> remove     def add_history(self, loss: List[float]) -> None:
 </s> add     def add_history(self, loss: T.List[float]) -> None: </s> remove     def _collate_and_store_loss(self, loss: List[float]) -> List[float]:
 </s> add     def _collate_and_store_loss(self, loss: T.List[float]) -> T.List[float]: </s> remove         self._sessions[self._session_id] = dict(timestamp=time.time(),
                                                no_logs=no_logs,
                                                loss_names=[],
                                                batchsize=0,
                                                iterations=0,
                                                config=config_changeable_items)

    def add_session_loss_names(self, loss_names: List[str]) -> None:
 </s> add         self._sessions[self._session_id] = {"timestamp": time.time(),
                                            "no_logs": no_logs,
                                            "loss_names": [],
                                            "batchsize": 0,
                                            "iterations": 0,
                                            "config": config_changeable_items}

    def add_session_loss_names(self, loss_names: T.List[str]) -> None: </s> remove         if get_backend() == "amd":
            # We can't log the graph in Tensorboard logs for AMD so need to obtain from state file
            loss_keys = {int(sess_id): [name for name in session["loss_names"] if name != "total"]
                         for sess_id, session in self._state["sessions"].items()}
        else:
            assert self._tb_logs is not None
            loss_keys = {sess_id: list(logs.keys())
                         for sess_id, logs
                         in self._tb_logs.get_loss(session_id=session_id).items()}
 </s> add         assert self._tb_logs is not None
        loss_keys = {sess_id: list(logs.keys())
                     for sess_id, logs
                     in self._tb_logs.get_loss(session_id=session_id).items()}
    def _log_tensorboard(self, loss: T.List[float]) -> None:
          if do_snapshot:
              self._model.snapshot()
          self._update_viewers(viewer, timelapse_kwargs)
  
    def _log_tensorboard(self, loss: T.List[float]) -> None:
          """ Log current loss to Tensorboard log files
  
          Parameters
          ----------
          loss: list
keep keep keep keep replace keep keep keep keep keep
 Remove plaidML support (#1325)

* Remove PlaidML reference from readme files

* Remove AMD option from installers

* remove amd requirements and update setup.py

* remove plaidml test from CI workflow

* gpustats: remove plaidml backend

* plaid removals:
  - faceswap.py - python version check
  - setup.cfg - plaidml typing ignore
  - lib.keras_utils - All plaid code
  - lib.launcher.py - All plaidml checks and configuration

* remove tf2.2 specific code from GUI event reader

* lib.model - remove all plaidml implementations

* plugins.extract - remove plaidml code

* plugins.train remove plaidml code

* lib.convert - remove plaidml code

* tools.model: remove plaidml code

* Remove plaidML tests from unit tests

* remove plaidml_utils and docsting cleanups

* Remove plaidML refs from configs

* fix keras imports 
plugins/train/trainer/_base.py