          var_x, style = self._inputs
          for i in range(self._dense_recursions):
              style = Dense(self._dense_nodes, kernel_initializer="he_normal")(style)
              if i != self._dense_recursions - 1:  # Don't add leakyReLu to final output
                  style = LeakyReLU(0.1)(style)
  
          # 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)
 
</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 = GaussianNoise(1.0)(var_x)
 </s> add         var_x = kl.GaussianNoise(1.0)(var_x) </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                 var_x = KConv2D(filters, 3, padding="same")(var_x)
 </s> add                 var_x = kl.Conv2D(filters, 3, padding="same")(var_x) </s> remove             styles = [Reshape([1, 1, filters])(Dense(filters)(style)) for _ in range(2)]
            noise = KConv2D(filters, 1, padding="same")(GaussianNoise(1.0)(var_x))
 </s> add             styles = [kl.Reshape([1, 1, filters])(kl.Dense(filters)(style)) for _ in range(2)]
            noise = kl.Conv2D(filters, 1, padding="same")(kl.GaussianNoise(1.0)(var_x))
                style = kl.LeakyReLU(0.1)(style)
          var_x, style = self._inputs
          for i in range(self._dense_recursions):
              style = Dense(self._dense_nodes, kernel_initializer="he_normal")(style)
              if i != self._dense_recursions - 1:  # Don't add leakyReLu to final output
                style = kl.LeakyReLU(0.1)(style)
  
          # 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)
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