  
          # Scale g_block filters to side dense
          g_filts = K.int_shape(var_x)[-1]
          var_x = Conv2D(g_filts, 3, strides=1, padding="same")(var_x)
          var_x = GaussianNoise(1.0)(var_x)
          var_x = self._g_block(var_x, style, g_filts)
          return KModel(self._inputs, var_x, name=f"g_block_{self._side}")
  
  
  class Decoder():  # pylint:disable=too-few-public-methods
 
</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                 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                 var_x = MaxPool2D(2, name=f"{name}_pool_{i}")(var_x)
 </s> add                 var_x = kl.MaxPool2D(2, name=f"{name}_pool_{i}")(var_x) </s> remove                 var_x = KConv2D(filters, 3, padding="same")(var_x)
 </s> add                 var_x = kl.Conv2D(filters, 3, padding="same")(var_x)
        var_x = kl.GaussianNoise(1.0)(var_x)
  
          # Scale g_block filters to side dense
          g_filts = K.int_shape(var_x)[-1]
          var_x = Conv2D(g_filts, 3, strides=1, padding="same")(var_x)
        var_x = kl.GaussianNoise(1.0)(var_x)
          var_x = self._g_block(var_x, style, g_filts)
          return KModel(self._inputs, var_x, name=f"g_block_{self._side}")
  
  
  class Decoder():  # pylint:disable=too-few-public-methods
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