              "Train lightweight model for 1 iteration with WTL, AutoClip, MixedPrecion",
              train_args("lightweight",
                         pathjoin(vid_base, "model"),
                         pathjoin(vid_base, "faces"),
                         iterations=_TRAIN_ARGS[0],
                         batchsize=_TRAIN_ARGS[1],
                         extra_args="-wl"))
  
          set_train_config(False)
          was_trained = run_test(
              "Train lightweight model for 1 iterations WITHOUT WTL, AutoClip, MixedPrecion",
 
</s> remove                        iterations=_TRAIN_ARGS[0],
                       batchsize=_TRAIN_ARGS[1],
 </s> add                        iterations=1,
                       batchsize=1, </s> remove     def add_mixed_precision_layers(self, layers: List[str]) -> None:
 </s> add     def add_mixed_precision_layers(self, layers: T.List[str]) -> None: </s> remove         masks3: Union[List[np.ndarray], np.ndarray] = []
 </s> add         masks3: T.Union[T.List[np.ndarray], np.ndarray] = [] </s> remove         except Exception as err:
            if get_backend() == "amd":
                # pylint:disable=import-outside-toplevel
                from lib.plaidml_utils import is_plaidml_error
                if (is_plaidml_error(err) and (
                        "CL_MEM_OBJECT_ALLOCATION_FAILURE" in str(err).upper() or
                        "enough memory for the current schedule" in str(err).lower())):
                    msg = ("You do not have enough GPU memory available to train the selected "
                           "model at the selected settings. You can try a number of things:"
                           "\n1) Close any other application that is using your GPU (web browsers "
                           "are particularly bad for this)."
                           "\n2) Lower the batchsize (the amount of images fed into the model "
                           "each iteration)."
                           "\n3) Use a more lightweight model, or select the model's 'LowMem' "
                           "option (in config) if it has one.")
                    raise FaceswapError(msg) from err
            raise
 </s> add  </s> remove         if get_backend() == "amd":  # Expand out AMD inputs + targets
            model_inputs = [inp for side in model_inputs for inp in side]  # type: ignore
            model_targets = [tgt for side in model_targets for tgt in side]  # type: ignore
 </s> add 
                       iterations=1,
                       batchsize=1,
              "Train lightweight model for 1 iteration with WTL, AutoClip, MixedPrecion",
              train_args("lightweight",
                         pathjoin(vid_base, "model"),
                         pathjoin(vid_base, "faces"),
                       iterations=1,
                       batchsize=1,
                       iterations=1,
                       batchsize=1,
                         extra_args="-wl"))
  
          set_train_config(False)
          was_trained = run_test(
              "Train lightweight model for 1 iterations WITHOUT WTL, AutoClip, MixedPrecion",
keep keep keep keep 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 
tests/simple_tests.py