          logger.debug("Initializing: %s (side: %s, input_shapes: %s)",
                       self.__class__.__name__, side, input_shapes)
          self._side = side
          self._config = config
          self._inputs = [Input(shape=shape) for shape in input_shapes]
          self._dense_nodes = 512
          self._dense_recursions = 3
          logger.debug("Initialized: %s", self.__class__.__name__)
  
      @classmethod
 
</s> remove     def _g_block(cls, inputs: Tensor, style: Tensor, filters: int, recursions: int = 2) -> Tensor:
 </s> add     def _g_block(cls,
                 inputs: tf.Tensor,
                 style: tf.Tensor,
                 filters: int,
                 recursions: int = 2) -> tf.Tensor: </s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </s> remove                 style = LeakyReLU(0.1)(style)
 </s> add                 style = kl.LeakyReLU(0.1)(style) </s> remove             style = Dense(self._dense_nodes, kernel_initializer="he_normal")(style)
 </s> add             style = kl.Dense(self._dense_nodes, kernel_initializer="he_normal")(style) </s> remove     def __init__(self, input_shape: tuple[int, ...], config: dict) -> None:
 </s> add     def __init__(self, input_shape: tuple[int, int, int], config: dict) -> None:
        self._inputs = [kl.Input(shape=shape) for shape in input_shapes]
          logger.debug("Initializing: %s (side: %s, input_shapes: %s)",
                       self.__class__.__name__, side, input_shapes)
          self._side = side
          self._config = config
        self._inputs = [kl.Input(shape=shape) for shape in input_shapes]
          self._dense_nodes = 512
          self._dense_recursions = 3
          logger.debug("Initialized: %s", self.__class__.__name__)
  
      @classmethod
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