  
  if get_backend() == "amd":
      from keras import applications as kapp, backend as K
      from keras.layers import (
          Add, BatchNormalization, Concatenate, Dense, Dropout, Flatten, GaussianNoise,
          GlobalAveragePooling2D, GlobalMaxPooling2D, Input, LeakyReLU, Reshape, UpSampling2D,
          Conv2D as KConv2D)
      from keras.models import clone_model
      # typing checks
      import keras
 
</s> remove         Add, BatchNormalization, Concatenate, Dense, Dropout, Flatten, GaussianNoise,
 </s> add         Add, BatchNormalization, Concatenate, Dense, Dropout, Flatten, GaussianNoise, MaxPool2D, </s> remove     UpscaleResizeImagesBlock)
 </s> add     UpscaleResizeImagesBlock, UpscaleDNYBlock) </s> remove              "Upsampling2D to upscale, saving about 1/3rd of VRAM of the heaviest methods.",
 </s> add              "Upsampling2D to upscale, saving about 1/3rd of VRAM of the heaviest methods."
             "\n\tupscale_dny - An alternative upscale implementation using Upsampling2D to "
             "upsale.",
        Add, BatchNormalization, Concatenate, Dense, Dropout, Flatten, GaussianNoise, MaxPool2D,
  
  if get_backend() == "amd":
      from keras import applications as kapp, backend as K
      from keras.layers import (
        Add, BatchNormalization, Concatenate, Dense, Dropout, Flatten, GaussianNoise, MaxPool2D,
          GlobalAveragePooling2D, GlobalMaxPooling2D, Input, LeakyReLU, Reshape, UpSampling2D,
          Conv2D as KConv2D)
      from keras.models import clone_model
      # typing checks
      import keras
keep keep keep keep replace keep keep keep keep keep
 Phaze-A - Updates for future implementations 
plugins/train/model/phaze_a.py