                                  self._config["bottleneck_norm"])
  
          dropout = f"{self._prefix}_dropout"
          for idx, nodes in enumerate(node_curve):
              var_x = Dropout(self._config[dropout], name=f"{dropout}_{idx + 1}")(var_x)
              var_x = Dense(nodes)(var_x)
  
          if self._side != "gblock":
              dim = self._config["fc_dimensions"]
              var_x = Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x)
              var_x = self._do_upsampling(var_x)
 
</s> remove             var_x = Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x)
 </s> add             var_x = kl.Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x) </s> remove             var_x = GaussianNoise(1.0)(var_x)
 </s> add             var_x = kl.GaussianNoise(1.0)(var_x) </s> remove             var_x = LeakyReLU(alpha=0.2)(var_x)
 </s> add             var_x = kl.LeakyReLU(alpha=0.2)(var_x) </s> remove                 style = LeakyReLU(0.1)(style)
 </s> add                 style = kl.LeakyReLU(0.1)(style) </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.Dropout(self._config[dropout], name=f"{dropout}_{idx + 1}")(var_x)
            var_x = kl.Dense(nodes)(var_x)
                                  self._config["bottleneck_norm"])
  
          dropout = f"{self._prefix}_dropout"
          for idx, nodes in enumerate(node_curve):
            var_x = kl.Dropout(self._config[dropout], name=f"{dropout}_{idx + 1}")(var_x)
            var_x = kl.Dense(nodes)(var_x)
            var_x = kl.Dropout(self._config[dropout], name=f"{dropout}_{idx + 1}")(var_x)
            var_x = kl.Dense(nodes)(var_x)
  
          if self._side != "gblock":
              dim = self._config["fc_dimensions"]
              var_x = Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x)
              var_x = self._do_upsampling(var_x)
keep keep keep keep replace 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