                   "layer": LayerNormalization,
                   "rms": RMSNormalization}
          return norms[self._config["dec_norm"]]()(inputs)
  
      def _dny_entry(self, inputs: Tensor) -> Tensor:
          """ Entry convolutions for using the upscale_dny method.
  
          Parameters
          ----------
          inputs: Tensor
 
</s> remove                  "layer": LayerNormalization,
 </s> add                  "layer": kl.LayerNormalization, </s> remove         norms = {"batch": BatchNormalization,
 </s> add         norms = {"batch": kl.BatchNormalization, </s> remove     def _do_upsampling(self, inputs: Tensor) -> Tensor:
 </s> add     def _do_upsampling(self, inputs: tf.Tensor) -> tf.Tensor: </s> remove     def _normalization(self, inputs: Tensor) -> Tensor:
 </s> add     def _normalization(self, inputs: tf.Tensor) -> tf.Tensor: </s> remove     def __call__(self, inputs: Tensor) -> Tensor:
 </s> add     def __call__(self, inputs: tf.Tensor) -> tf.Tensor:
    def _dny_entry(self, inputs: tf.Tensor) -> tf.Tensor:
                   "layer": LayerNormalization,
                   "rms": RMSNormalization}
          return norms[self._config["dec_norm"]]()(inputs)
  
    def _dny_entry(self, inputs: tf.Tensor) -> tf.Tensor:
          """ Entry convolutions for using the upscale_dny method.
  
          Parameters
          ----------
          inputs: Tensor
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