          feed_batch = [feed_face for item in batch for feed_face in item.feed_faces]
          if faces_seen != 0:
              feed_faces = self._compile_feed_faces(feed_batch)
              batch_size = None
              if get_backend() == "amd" and feed_faces.shape[0] != self._batchsize:
                  logger.verbose("Fallback to BS=1")  # type:ignore
                  batch_size = 1
              predicted = self._predict(feed_faces, batch_size)
          else:
              predicted = np.array([])
  
          self._queue_out_frames(batch, predicted)
 
</s> remove                               autoclip,
 </s> add                               self.config["autoclip"], </s> remove             retval = (os.path.basename(prefix) == "envs")
 </s> add             retval = os.path.basename(prefix) == "envs" </s> remove         autoclip = get_backend() != "amd" and self.config["autoclip"]
 </s> add  </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()} </s> remove         if backend != "amd" and tf_ver < min_ver:
 </s> add         if tf_ver < min_ver:
          feed_batch = [feed_face for item in batch for feed_face in item.feed_faces]
          if faces_seen != 0:
              feed_faces = self._compile_feed_faces(feed_batch)
              batch_size = None



              predicted = self._predict(feed_faces, batch_size)
          else:
              predicted = np.array([])
  
          self._queue_out_frames(batch, predicted)
keep keep keep keep replace replace 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 
scripts/convert.py