          num_ups_in_fc = self._config["dec_upscales_in_fc"]
  
          if self._config["learn_mask"] and num_ups_in_fc:
              # Mask has already been created in FC and is an output of that model
              inputs = [inputs, Input(shape=self._input_shape)]
  
          indicies = None if not num_ups_in_fc else (num_ups_in_fc, -1)
          upscales = UpscaleBlocks(self._side,
                                   self._config,
                                   layer_indicies=indicies)(inputs)
 
</s> remove         inputs = Input(shape=self._input_shape)
 </s> add         inputs = kl.Input(shape=self._input_shape) </s> remove         if model.keras_name:
 </s> add         if model.keras_name and self._config["enc_architecture"].startswith("clipv_"):
            assert model.keras_name in T.get_args(TypeModelsViT)
            kwargs["input_shape"] = self._input_shape
            kwargs["load_weights"] = self._config["enc_load_weights"]
            retval = ViT(T.cast(TypeModelsViT, model.keras_name),
                         input_size=self._input_shape[0],
                         load_weights=self._config["enc_load_weights"])()
        elif model.keras_name: </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 = GaussianNoise(1.0)(var_x)
 </s> add             var_x = kl.GaussianNoise(1.0)(var_x) </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:
            inputs = [inputs, kl.Input(shape=self._input_shape)]
          num_ups_in_fc = self._config["dec_upscales_in_fc"]
  
          if self._config["learn_mask"] and num_ups_in_fc:
              # Mask has already been created in FC and is an output of that model
            inputs = [inputs, kl.Input(shape=self._input_shape)]
  
          indicies = None if not num_ups_in_fc else (num_ups_in_fc, -1)
          upscales = UpscaleBlocks(self._side,
                                   self._config,
                                   layer_indicies=indicies)(inputs)
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