      vgg19=_EncoderInfo(
          keras_name="VGG19", color_order="bgr", scaling=(0, 255), default_size=224),
      xception=_EncoderInfo(
          keras_name="Xception", scaling=(-1, 1), min_size=71, default_size=299),
      fs_original=_EncoderInfo(
          keras_name="", color_order="bgr", min_size=32, default_size=1024))
  
  
  class Model(ModelBase):
      """ Phaze-A Faceswap Model.
  
 
</s> remove     xception=_EncoderInfo(
 </s> add     "xception": _EncoderInfo( </s> remove     vgg19=_EncoderInfo(
 </s> add     "vgg19": _EncoderInfo( </s> remove     resnet50_v2=_EncoderInfo(
        keras_name="ResNet50V2", no_amd=True, scaling=(-1, 1), default_size=224),
    resnet101=_EncoderInfo(
        keras_name="ResNet101", no_amd=True, scaling=(-1, 1), default_size=224),
    resnet101_v2=_EncoderInfo(
        keras_name="ResNet101V2", no_amd=True, scaling=(-1, 1), default_size=224),
    resnet152=_EncoderInfo(
        keras_name="ResNet152", no_amd=True, scaling=(-1, 1), default_size=224),
    resnet152_v2=_EncoderInfo(
        keras_name="ResNet152V2", no_amd=True, scaling=(-1, 1), default_size=224),
    vgg16=_EncoderInfo(
 </s> add     "resnet50_v2": _EncoderInfo(
        keras_name="ResNet50V2", scaling=(-1, 1), default_size=224),
    "resnet101": _EncoderInfo(
        keras_name="ResNet101", scaling=(-1, 1), default_size=224),
    "resnet101_v2": _EncoderInfo(
        keras_name="ResNet101V2", scaling=(-1, 1), default_size=224),
    "resnet152": _EncoderInfo(
        keras_name="ResNet152", scaling=(-1, 1), default_size=224),
    "resnet152_v2": _EncoderInfo(
        keras_name="ResNet152V2", scaling=(-1, 1), default_size=224),
    "vgg16": _EncoderInfo( </s> remove     efficientnet_b0=_EncoderInfo(
        keras_name="EfficientNetB0",
        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=224),
    efficientnet_b1=_EncoderInfo(
        keras_name="EfficientNetB1",
        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=240),
    efficientnet_b2=_EncoderInfo(
        keras_name="EfficientNetB2",
        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=260),
    efficientnet_b3=_EncoderInfo(
        keras_name="EfficientNetB3",
        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=300),
    efficientnet_b4=_EncoderInfo(
        keras_name="EfficientNetB4",
        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=380),
    efficientnet_b5=_EncoderInfo(
        keras_name="EfficientNetB5",
        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=456),
    efficientnet_b6=_EncoderInfo(
        keras_name="EfficientNetB6",
        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=528),
    efficientnet_b7=_EncoderInfo(
        keras_name="EfficientNetB7",
        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=600),
    efficientnet_v2_b0=_EncoderInfo(
        keras_name="EfficientNetV2B0",
        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=224),
    efficientnet_v2_b1=_EncoderInfo(
        keras_name="EfficientNetV2B1",
        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=240),
    efficientnet_v2_b2=_EncoderInfo(
        keras_name="EfficientNetV2B2",
        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=260),
    efficientnet_v2_b3=_EncoderInfo(
        keras_name="EfficientNetV2B3",
        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=300),
    efficientnet_v2_s=_EncoderInfo(
        keras_name="EfficientNetV2S",
        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=384),
    efficientnet_v2_m=_EncoderInfo(
        keras_name="EfficientNetV2M",
        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=480),
    efficientnet_v2_l=_EncoderInfo(
        keras_name="EfficientNetV2L",
        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=480),
    inception_resnet_v2=_EncoderInfo(
 </s> add     "efficientnet_b0": _EncoderInfo(
        keras_name="EfficientNetB0", tf_min=(2, 3), scaling=(0, 255), default_size=224),
    "efficientnet_b1": _EncoderInfo(
        keras_name="EfficientNetB1", tf_min=(2, 3), scaling=(0, 255), default_size=240),
    "efficientnet_b2": _EncoderInfo(
        keras_name="EfficientNetB2", tf_min=(2, 3), scaling=(0, 255), default_size=260),
    "efficientnet_b3": _EncoderInfo(
        keras_name="EfficientNetB3", tf_min=(2, 3), scaling=(0, 255), default_size=300),
    "efficientnet_b4": _EncoderInfo(
        keras_name="EfficientNetB4", tf_min=(2, 3), scaling=(0, 255), default_size=380),
    "efficientnet_b5": _EncoderInfo(
        keras_name="EfficientNetB5", tf_min=(2, 3), scaling=(0, 255), default_size=456),
    "efficientnet_b6": _EncoderInfo(
        keras_name="EfficientNetB6", tf_min=(2, 3), scaling=(0, 255), default_size=528),
    "efficientnet_b7": _EncoderInfo(
        keras_name="EfficientNetB7", tf_min=(2, 3), scaling=(0, 255), default_size=600),
    "efficientnet_v2_b0": _EncoderInfo(
        keras_name="EfficientNetV2B0", tf_min=(2, 8), scaling=(-1, 1), default_size=224),
    "efficientnet_v2_b1": _EncoderInfo(
        keras_name="EfficientNetV2B1", tf_min=(2, 8), scaling=(-1, 1), default_size=240),
    "efficientnet_v2_b2": _EncoderInfo(
        keras_name="EfficientNetV2B2", tf_min=(2, 8), scaling=(-1, 1), default_size=260),
    "efficientnet_v2_b3": _EncoderInfo(
        keras_name="EfficientNetV2B3", tf_min=(2, 8), scaling=(-1, 1), default_size=300),
    "efficientnet_v2_s": _EncoderInfo(
        keras_name="EfficientNetV2S", tf_min=(2, 8), scaling=(-1, 1), default_size=384),
    "efficientnet_v2_m": _EncoderInfo(
        keras_name="EfficientNetV2M", tf_min=(2, 8), scaling=(-1, 1), default_size=480),
    "efficientnet_v2_l": _EncoderInfo(
        keras_name="EfficientNetV2L", tf_min=(2, 8), scaling=(-1, 1), default_size=480),
    "inception_resnet_v2": _EncoderInfo( </s> remove     mobilenet=_EncoderInfo(
 </s> add     "mobilenet": _EncoderInfo(
    "fs_original": _EncoderInfo(
        keras_name="", color_order="bgr", min_size=32, default_size=1024)}
      vgg19=_EncoderInfo(
          keras_name="VGG19", color_order="bgr", scaling=(0, 255), default_size=224),
      xception=_EncoderInfo(
          keras_name="Xception", scaling=(-1, 1), min_size=71, default_size=299),
    "fs_original": _EncoderInfo(
        keras_name="", color_order="bgr", min_size=32, default_size=1024)}
    "fs_original": _EncoderInfo(
        keras_name="", color_order="bgr", min_size=32, default_size=1024)}
  
  
  class Model(ModelBase):
      """ Phaze-A Faceswap Model.
  
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 
plugins/train/model/phaze_a.py