          var_x = Flatten()(var_x)
      var_x = bottlenecks[bottleneck](var_x)
      if len(K.int_shape(var_x)[1:]) > 1:
          # Flatten prior to fc layers
          var_x = Flatten()(var_x)
      return var_x
  
  
  def _get_upscale_layer(method: T.Literal["resize_images", "subpixel", "upscale_dny",
                                           "upscale_fast", "upscale_hybrid", "upsample2d"],
 
</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     bottlenecks = {"average_pooling": GlobalAveragePooling2D(),
                   "dense": Dense(size),
                   "max_pooling": GlobalMaxPooling2D()}
 </s> add     bottlenecks = {"average_pooling": kl.GlobalAveragePooling2D(),
                   "dense": kl.Dense(size),
                   "max_pooling": kl.GlobalMaxPooling2D()} </s> remove                        interpolation: str | None = None) -> keras.layers.Layer:
 </s> add                        interpolation: str | None = None) -> tf.keras.layers.Layer: </s> remove                 style = LeakyReLU(0.1)(style)
 </s> add                 style = kl.LeakyReLU(0.1)(style) </s> remove                 var_x = MaxPool2D(2, name=f"{name}_pool_{i}")(var_x)
 </s> add                 var_x = kl.MaxPool2D(2, name=f"{name}_pool_{i}")(var_x)
        var_x = kl.Flatten()(var_x)
          var_x = Flatten()(var_x)
      var_x = bottlenecks[bottleneck](var_x)
      if len(K.int_shape(var_x)[1:]) > 1:
          # Flatten prior to fc layers
        var_x = kl.Flatten()(var_x)
      return var_x
  
  
  def _get_upscale_layer(method: T.Literal["resize_images", "subpixel", "upscale_dny",
                                           "upscale_fast", "upscale_hybrid", "upsample2d"],
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