          The name of the encoder in Keras Applications. Empty string `""` if the encoder does not
          exist in Keras Applications
      default_size: int
          The default input size of the encoder
      no_amd: bool, optional
          ``True`` if the encoder is not compatible with the PlaidML backend otherwise ``False``.
          Default: ``False``
      tf_min: float, optional
          The lowest version of Tensorflow that the encoder can be used for. Default: `2.0`
      scaling: tuple, optional
          The float scaling that the encoder expects. Default: `(0, 1)`
      min_size: int, optional
 
</s> remove     fs_original_depth=dict(
        default=4,
        info="Faceswap Encoder only: The number of convolutions to perform within the encoder.",
        datatype=int,
        min_max=(2, 10),
        rounding=1,
        group="faceswap encoder configuration",
        fixed=True),
    fs_original_min_filters=dict(
        default=128,
        info="Faceswap Encoder only: The minumum number of filters to use for encoder "
             "convolutions. (i.e. the number of filters to use for the first encoder layer).",
        datatype=int,
        min_max=(16, 2048),
        rounding=64,
        group="faceswap encoder configuration",
        fixed=True),
    fs_original_max_filters=dict(
        default=1024,
        info="Faceswap Encoder only: The maximum number of filters to use for encoder "
             "convolutions. (i.e. the number of filters to use for the final encoder layer).",
        datatype=int,
        min_max=(256, 8192),
        rounding=128,
        group="faceswap encoder configuration",
        fixed=True),
    fs_original_use_alt=dict(
        default=False,
        info="Use a slightly alternate version of the Faceswap Encoder."
             "\n\tTrue - Use the alternate variation of the Faceswap Encoder."
             "\n\tFalse - Use the original Faceswap Encoder.",
        datatype=bool,
        group="faceswap encoder configuration",
        fixed=True),
 </s> add     "fs_original_depth": {
        "default": 4,
        "info": "Faceswap Encoder only: The number of convolutions to perform within the encoder.",
        "datatype": int,
        "min_max": (2, 10),
        "rounding": 1,
        "group": "faceswap encoder configuration",
        "fixed": True},
    "fs_original_min_filters": {
        "default": 128,
        "info": (
            "Faceswap Encoder only: The minumum number of filters to use for encoder "
            "convolutions. (i.e. the number of filters to use for the first encoder layer)."),
        "datatype": int,
        "min_max": (16, 2048),
        "rounding": 64,
        "group": "faceswap encoder configuration",
        "fixed": True},
    "fs_original_max_filters": {
        "default": 1024,
        "info": (
            "Faceswap Encoder only: The maximum number of filters to use for encoder "
            "convolutions. (i.e. the number of filters to use for the final encoder layer)."),
        "datatype": int,
        "min_max": (256, 8192),
        "rounding": 128,
        "group": "faceswap encoder configuration",
        "fixed": True},
    "fs_original_use_alt": {
        "default": False,
        "info": (
            "Use a slightly alternate version of the Faceswap Encoder."
            "\n\tTrue - Use the alternate variation of the Faceswap Encoder."
            "\n\tFalse - Use the original Faceswap Encoder."),
        "datatype": bool,
        "group": "faceswap encoder configuration",
        "fixed": True}, </s> remove         if get_backend() == "amd" and model.no_amd:
            valid = [k for k, v in _MODEL_MAPPING.items() if not v.no_amd]
            raise FaceswapError(f"'{arch}' is not compatible with the AMD backend. Choose one of "
                                f"{valid}.")

 </s> add  </s> remove     no_amd: bool = False
    tf_min: Tuple[int, int] = (2, 0)
    scaling: Tuple[int, int] = (0, 1)
 </s> add     tf_min: T.Tuple[int, int] = (2, 0)
    scaling: T.Tuple[int, int] = (0, 1) </s> remove             The output of this function must be a keras model generated from
            :class:`plugins.train.model._base.KerasModel`. See Keras documentation for the correct
            structure, but note that parameter :attr:`name` is a required rather than an optional
            argument in Faceswap. You should assign this to the attribute ``self.name`` that is
            automatically generated from the plugin's filename.
 </s> add             See Keras documentation for the correct structure, but note that parameter :attr:`name`
            is a required rather than an optional argument in Faceswap. You should assign this to
            the attribute ``self.name`` that is automatically generated from the plugin's filename. </s> remove                  model_path: List[str],
 </s> add                  model_path: T.List[str],
          The name of the encoder in Keras Applications. Empty string `""` if the encoder does not
          exist in Keras Applications
      default_size: int
          The default input size of the encoder



      tf_min: float, optional
          The lowest version of Tensorflow that the encoder can be used for. Default: `2.0`
      scaling: tuple, optional
          The float scaling that the encoder expects. Default: `(0, 1)`
      min_size: int, optional
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 
plugins/train/model/phaze_a.py