          bool
              ``True`` if mixed precision has been enabled otherwise ``False``
          """
          logger.debug("use_mixed_precision: %s", use_mixed_precision)
          if get_backend() == "amd":
              logger.debug("No action to perform for 'mixed_precision' on backend '%s': "
                           "use_mixed_precision: %s)", get_backend(), use_mixed_precision)
              return False
  
          if not use_mixed_precision:
              policy = mixedprecision.Policy('float32')
              mixedprecision.set_global_policy(policy)
              logger.debug("Disabling mixed precision. (Compute dtype: %s, variable_dtype: %s)",
                           policy.compute_dtype, policy.variable_dtype)
 
</s> remove             policy = mixedprecision.Policy('float32')
            mixedprecision.set_global_policy(policy)
 </s> add             policy = mixedprecision.Policy('float32')  # pylint:disable=no-member
            mixedprecision.set_global_policy(policy)  # pylint:disable=no-member </s> remove         policy = mixedprecision.Policy('mixed_float16')
        mixedprecision.set_global_policy(policy)
 </s> add         policy = mixedprecision.Policy('mixed_float16')  # pylint:disable=no-member
        mixedprecision.set_global_policy(policy)  # pylint:disable=no-member </s> remove 
        Notes
        -----
        Clip-norm is ballooning VRAM usage, which is not expected behavior and may be a bug in
        Keras/Tensorflow.

        PlaidML has a bug regarding the clip-norm parameter See:
        https://github.com/plaidml/plaidml/issues/228. We workaround by simply not adding this
        parameter for AMD backend users.
 </s> add  </s> remove         if get_backend() == "amd":
            logger.debug("Mixed Precision not supported for AMD. Returning empty list")
            return []
 </s> add  </s> remove 
        if get_backend() == "amd" and not self._setup_amd(arguments):
            safe_shutdown(got_error=True)

    @classmethod
    def _setup_amd(cls, arguments: "argparse.Namespace") -> bool:
        """ Test for plaidml and perform setup for AMD.

        Parameters
        ----------
        arguments: :class:`argparse.Namespace`
            The command line arguments passed to Faceswap.

        Returns
        -------
        bool
            ``True`` if AMD was set up succesfully otherwise ``False``
        """
        logger.debug("Setting up for AMD")
        if platform.system() == "Windows":
            deprecation_warning("The AMD backend",
                                additional_info="Please consider re-installing using the "
                                                "'DirectML' backend")
        try:
            import plaidml  # noqa pylint:disable=unused-import,import-outside-toplevel
        except ImportError:
            logger.error("PlaidML not found. Run `pip install plaidml-keras` for AMD support")
            return False
        from lib.gpu_stats import setup_plaidml  # pylint:disable=import-outside-toplevel
        setup_plaidml(arguments.loglevel, arguments.exclude_gpus)
        logger.debug("setup up for PlaidML")
        return True
 </s> add 
          bool
              ``True`` if mixed precision has been enabled otherwise ``False``
          """
          logger.debug("use_mixed_precision: %s", use_mixed_precision)





          if not use_mixed_precision:
              policy = mixedprecision.Policy('float32')
              mixedprecision.set_global_policy(policy)
              logger.debug("Disabling mixed precision. (Compute dtype: %s, variable_dtype: %s)",
                           policy.compute_dtype, policy.variable_dtype)
keep keep keep keep replace replace 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 
plugins/train/model/_base/settings.py