                  var_x = UpscaleBlocks(self._side,
                                        self._config,
                                        layer_indicies=(0, num_upscales))(var_x)
  
          return KModel(input_, var_x, name=f"fc_{self._side}")
  
  
  class UpscaleBlocks():  # pylint: disable=too-few-public-methods
      """ Obtain a block of upscalers.
  
 
</s> remove         return KModel(self._inputs, var_x, name=f"g_block_{self._side}")
 </s> add         return keras.models.Model(self._inputs, var_x, name=f"g_block_{self._side}") </s> remove             inputs = [inputs, Input(shape=self._input_shape)]
 </s> add             inputs = [inputs, kl.Input(shape=self._input_shape)] </s> remove         var_x = GaussianNoise(1.0)(var_x)
 </s> add         var_x = kl.GaussianNoise(1.0)(var_x) </s> remove         return KModel(input_, var_x, name="encoder")
 </s> add         return keras.models.Model(input_, var_x, name="encoder")
        return keras.models.Model(input_, var_x, name=f"fc_{self._side}")
                  var_x = UpscaleBlocks(self._side,
                                        self._config,
                                        layer_indicies=(0, num_upscales))(var_x)
  
        return keras.models.Model(input_, var_x, name=f"fc_{self._side}")
  
  
  class UpscaleBlocks():  # pylint: disable=too-few-public-methods
      """ Obtain a block of upscalers.
  
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