              old_shape = K.int_shape(inputs)[1:]
              new_shape = (new_dim, new_dim, np.prod(old_shape) // new_dim ** 2)
              logger.debug("Reshaping tensor from %s to %s for output size %s",
                           K.int_shape(inputs)[1:], new_shape, self._config["output_size"])
              var_x = Reshape(new_shape)(var_x)
          return var_x
  
      def _upscale_block(self,
                         inputs: Tensor,
                         filters: int,
 
</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                        inputs: Tensor,
 </s> add                        inputs: tf.Tensor, </s> remove                 if not isinstance(layer, Dropout):
 </s> add                 if not isinstance(layer, kl.Dropout): </s> remove                        is_mask: bool = False) -> Tensor:
 </s> add                        is_mask: bool = False) -> tf.Tensor: </s> remove         self._inputs = [Input(shape=shape) for shape in input_shapes]
 </s> add         self._inputs = [kl.Input(shape=shape) for shape in input_shapes]
            var_x = kl.Reshape(new_shape)(var_x)
              old_shape = K.int_shape(inputs)[1:]
              new_shape = (new_dim, new_dim, np.prod(old_shape) // new_dim ** 2)
              logger.debug("Reshaping tensor from %s to %s for output size %s",
                           K.int_shape(inputs)[1:], new_shape, self._config["output_size"])
            var_x = kl.Reshape(new_shape)(var_x)
          return var_x
  
      def _upscale_block(self,
                         inputs: Tensor,
                         filters: int,
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