          keras_name="MobileNetV3Small",
          no_amd=True, tf_min=(2, 4), scaling=(-1, 1), default_size=224),
      nasnet_large=_EncoderInfo(
          keras_name="NASNetLarge", scaling=(-1, 1), default_size=331, enforce_for_weights=True),
      nasnet_mobile=_EncoderInfo(
          keras_name="NASNetMobile", scaling=(-1, 1), default_size=224, enforce_for_weights=True),
      resnet50=_EncoderInfo(
          keras_name="ResNet50", scaling=(-1, 1), min_size=32, default_size=224),
      resnet50_v2=_EncoderInfo(
          keras_name="ResNet50V2", no_amd=True, scaling=(-1, 1), default_size=224),
 
</s> remove     resnet50=_EncoderInfo(
 </s> add     "resnet50": _EncoderInfo( </s> remove     mobilenet_v3_large=_EncoderInfo(
        keras_name="MobileNetV3Large",
        no_amd=True, tf_min=(2, 4), scaling=(-1, 1), default_size=224),
    mobilenet_v3_small=_EncoderInfo(
        keras_name="MobileNetV3Small",
        no_amd=True, tf_min=(2, 4), scaling=(-1, 1), default_size=224),
    nasnet_large=_EncoderInfo(
 </s> add     "mobilenet_v3_large": _EncoderInfo(
        keras_name="MobileNetV3Large", tf_min=(2, 4), scaling=(-1, 1), default_size=224),
    "mobilenet_v3_small": _EncoderInfo(
        keras_name="MobileNetV3Small", tf_min=(2, 4), scaling=(-1, 1), default_size=224),
    "nasnet_large": _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     mobilenet_v2=_EncoderInfo(
 </s> add     "mobilenet_v2": _EncoderInfo( </s> remove     inception_v3=_EncoderInfo(
 </s> add     "inception_v3": _EncoderInfo(
    "nasnet_mobile": _EncoderInfo(
          keras_name="MobileNetV3Small",
          no_amd=True, tf_min=(2, 4), scaling=(-1, 1), default_size=224),
      nasnet_large=_EncoderInfo(
          keras_name="NASNetLarge", scaling=(-1, 1), default_size=331, enforce_for_weights=True),
    "nasnet_mobile": _EncoderInfo(
          keras_name="NASNetMobile", scaling=(-1, 1), default_size=224, enforce_for_weights=True),
      resnet50=_EncoderInfo(
          keras_name="ResNet50", scaling=(-1, 1), min_size=32, default_size=224),
      resnet50_v2=_EncoderInfo(
          keras_name="ResNet50V2", no_amd=True, scaling=(-1, 1), default_size=224),
keep keep keep keep 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