     ~lib.model.layers.GlobalStdDevPooling2D
     ~lib.model.layers.KResizeImages
     ~lib.model.layers.L2_normalize
     ~lib.model.layers.PixelShuffler
     ~lib.model.layers.ReflectionPadding2D
     ~lib.model.layers.SubPixelUpscaling
     ~lib.model.layers.Swish
  
  .. automodule:: lib.model.layers
 
</s> remove             var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add             var_x = kl.LeakyReLU(alpha=0.1)(var_x) </s> remove     bottleneck: str
        The type of layer to use for the bottleneck
 </s> add     bottleneck: str or ``None``
        The type of layer to use for the bottleneck. ``None`` to not use a bottleneck </s> remove     norms = {"layer": LayerNormalization,
 </s> add     norms = {"layer": kl.LayerNormalization, </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     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:
   ~lib.model.layers.QuickGELU
     ~lib.model.layers.GlobalStdDevPooling2D
     ~lib.model.layers.KResizeImages
     ~lib.model.layers.L2_normalize
     ~lib.model.layers.PixelShuffler
   ~lib.model.layers.QuickGELU
     ~lib.model.layers.ReflectionPadding2D
     ~lib.model.layers.SubPixelUpscaling
     ~lib.model.layers.Swish
  
  .. automodule:: lib.model.layers
keep keep keep add 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 
docs/full/lib/model.rst