                                                upsample_filts,
                                                activation="leakyrelu")
                  var_x = upscaler(var_x)
          if upsampler == "upsample2d":
              var_x = LeakyReLU(alpha=0.1)(var_x)
          return var_x
  
      def __call__(self) -> keras.models.Model:
          """ Call the intermediate layer.
  
 
</s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </s> remove             var_x = Add()([var_x, noise])
            var_x = LeakyReLU(0.2)(var_x)
 </s> add             var_x = kl.Add()([var_x, noise])
            var_x = kl.LeakyReLU(0.2)(var_x) </s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </s> remove                 var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add                 var_x = kl.LeakyReLU(alpha=0.1)(var_x) </s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model:
            var_x = kl.LeakyReLU(alpha=0.1)(var_x)
                                                upsample_filts,
                                                activation="leakyrelu")
                  var_x = upscaler(var_x)
          if upsampler == "upsample2d":
            var_x = kl.LeakyReLU(alpha=0.1)(var_x)
          return var_x
  
      def __call__(self) -> keras.models.Model:
          """ Call the intermediate layer.
  
keep keep keep keep replace keep 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