              The output tensor from the G-Block model
          """
          var_x = inputs
          for i in range(recursions):
              styles = [Reshape([1, 1, filters])(Dense(filters)(style)) for _ in range(2)]
              noise = KConv2D(filters, 1, padding="same")(GaussianNoise(1.0)(var_x))
  
              if i == recursions - 1:
                  var_x = KConv2D(filters, 3, padding="same")(var_x)
  
              var_x = AdaInstanceNormalization(dtype="float32")([var_x, *styles])
 
</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             var_x = Add()([var_x, noise])
            var_x = LeakyReLU(0.2)(var_x)
 </s> add             var_x = kl.Add()([var_x, noise])
            var_x = kl.LeakyReLU(0.2)(var_x) </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         inputs = Input(shape=self._input_shape)
 </s> add         inputs = kl.Input(shape=self._input_shape)
            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))
              The output tensor from the G-Block model
          """
          var_x = inputs
          for i in range(recursions):
            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))
            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))
  
              if i == recursions - 1:
                  var_x = KConv2D(filters, 3, padding="same")(var_x)
  
              var_x = AdaInstanceNormalization(dtype="float32")([var_x, *styles])
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