      UpscaleResizeImagesBlock, UpscaleDNYBlock)
  from lib.model.normalization import (
      AdaInstanceNormalization, GroupNormalization, InstanceNormalization, RMSNormalization)
  from lib.utils import get_tf_version, FaceswapError
  
  from ._base import ModelBase, get_all_sub_models
  
 
</s> remove if T.TYPE_CHECKING:
    from tensorflow import keras
    from tensorflow import Tensor

 </s> add  </s> remove # Ignore linting errors from Tensorflow's thoroughly broken import system
from tensorflow.keras.layers import LayerNormalization  # pylint:disable=import-error
from tensorflow.keras import applications as kapp, backend as K  # noqa:E501  # pylint:disable=import-error
from tensorflow.keras.layers import (  # pylint:disable=import-error
    Add, BatchNormalization, Concatenate, Dense, Dropout, Flatten, GaussianNoise, MaxPool2D,
    GlobalAveragePooling2D, GlobalMaxPooling2D, Input, LeakyReLU, Reshape, UpSampling2D,
    Conv2D as KConv2D)
from tensorflow.keras.models import clone_model, Model as KModel  # noqa:E501  # pylint:disable=import-error
 </s> add import tensorflow as tf </s> remove from lib.model.nets import AlexNet, SqueezeNet
 </s> add from lib.model.networks import AlexNet, SqueezeNet </s> remove         norms = {"batch": BatchNormalization,
 </s> add         norms = {"batch": kl.BatchNormalization,
from lib.model.networks import ViT, TypeModelsViT
      UpscaleResizeImagesBlock, UpscaleDNYBlock)
  from lib.model.normalization import (
      AdaInstanceNormalization, GroupNormalization, InstanceNormalization, RMSNormalization)
from lib.model.networks import ViT, TypeModelsViT
  from lib.utils import get_tf_version, FaceswapError
  
  from ._base import ModelBase, get_all_sub_models
  
keep keep add keep keep keep keep
 Updates:
    - Remove clipfaker
    - Add clip to Phaze-A
    - streamline clip to vision only
    - update lib.model.networks
    - Docs and tests update
    - Phaze-A - add option to flatten bottleneck 
plugins/train/model/phaze_a.py