              if i == recursions - 1:
                  var_x = KConv2D(filters, 3, padding="same")(var_x)
  
              var_x = AdaInstanceNormalization(dtype="float32")([var_x, *styles])
              var_x = Add()([var_x, noise])
              var_x = LeakyReLU(0.2)(var_x)
  
          return var_x
  
      def __call__(self) -> keras.models.Model:
          """ G-Block Network.
 
</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)) </s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </s> remove                 style = LeakyReLU(0.1)(style)
 </s> add                 style = kl.LeakyReLU(0.1)(style) </s> remove             var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add             var_x = kl.LeakyReLU(alpha=0.1)(var_x)
            var_x = kl.Add()([var_x, noise])
            var_x = kl.LeakyReLU(0.2)(var_x)
              if i == recursions - 1:
                  var_x = KConv2D(filters, 3, padding="same")(var_x)
  
              var_x = AdaInstanceNormalization(dtype="float32")([var_x, *styles])
            var_x = kl.Add()([var_x, noise])
            var_x = kl.LeakyReLU(0.2)(var_x)
            var_x = kl.Add()([var_x, noise])
            var_x = kl.LeakyReLU(0.2)(var_x)
  
          return var_x
  
      def __call__(self) -> keras.models.Model:
          """ G-Block Network.
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