                                        interpolation="bilinear")
  
          var_x = upscaler(inputs)
          if not is_mask and self._config["dec_gaussian"]:
              var_x = GaussianNoise(1.0)(var_x)
          if not is_mask and self._config["dec_res_blocks"] and not skip_residual:
              var_x = self._normalization(var_x)
              var_x = LeakyReLU(alpha=0.2)(var_x)
              for _ in range(self._config["dec_res_blocks"]):
                  var_x = ResidualBlock(filters)(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                 var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add                 var_x = kl.LeakyReLU(alpha=0.1)(var_x) </s> remove             inputs = [inputs, Input(shape=self._input_shape)]
 </s> add             inputs = [inputs, kl.Input(shape=self._input_shape)] </s> remove     if bottleneck == "dense" and len(K.int_shape(var_x)[1:]) > 1:
        # Flatten non-1D inputs for dense bottleneck
        var_x = Flatten()(var_x)
    var_x = bottlenecks[bottleneck](var_x)
    if len(K.int_shape(var_x)[1:]) > 1:
 </s> add     if bottleneck == "dense" and K.ndim(var_x) > 2:  # Flatten non-1D inputs for dense
        var_x = kl.Flatten()(var_x)
    if bottleneck != "flatten":
        var_x = bottlenecks[bottleneck](var_x)
    if K.ndim(var_x) > 2: </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)
                                        interpolation="bilinear")
  
          var_x = upscaler(inputs)
          if not is_mask and self._config["dec_gaussian"]:
            var_x = kl.GaussianNoise(1.0)(var_x)
          if not is_mask and self._config["dec_res_blocks"] and not skip_residual:
              var_x = self._normalization(var_x)
              var_x = LeakyReLU(alpha=0.2)(var_x)
              for _ in range(self._config["dec_res_blocks"]):
                  var_x = ResidualBlock(filters)(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