          if upsamples:
              kwargs["size"] = upsamples
          if interpolation:
              kwargs["interpolation"] = interpolation
          return UpSampling2D(**kwargs)
      if method == "subpixel":
          return UpscaleBlock(filters, activation=activation)
      if method == "upscale_fast":
          return Upscale2xBlock(filters, activation=activation, fast=True)
      if method == "upscale_hybrid":
 
</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 = LeakyReLU(alpha=0.1)(var_x)
 </s> add             var_x = kl.LeakyReLU(alpha=0.1)(var_x) </s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </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                 var_x = KConv2D(filters, 3, padding="same")(var_x)
 </s> add                 var_x = kl.Conv2D(filters, 3, padding="same")(var_x)
        return kl.UpSampling2D(**kwargs)
          if upsamples:
              kwargs["size"] = upsamples
          if interpolation:
              kwargs["interpolation"] = interpolation
        return kl.UpSampling2D(**kwargs)
      if method == "subpixel":
          return UpscaleBlock(filters, activation=activation)
      if method == "upscale_fast":
          return Upscale2xBlock(filters, activation=activation, fast=True)
      if method == "upscale_hybrid":
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