              var_x = Dense(nodes)(var_x)
  
          if self._side != "gblock":
              dim = self._config["fc_dimensions"]
              var_x = Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x)
              var_x = self._do_upsampling(var_x)
  
              num_upscales = self._config["dec_upscales_in_fc"]
              if num_upscales:
                  var_x = UpscaleBlocks(self._side,
 
</s> remove             var_x = Dropout(self._config[dropout], name=f"{dropout}_{idx + 1}")(var_x)
            var_x = Dense(nodes)(var_x)
 </s> add             var_x = kl.Dropout(self._config[dropout], name=f"{dropout}_{idx + 1}")(var_x)
            var_x = kl.Dense(nodes)(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: </s> remove             var_x = GaussianNoise(1.0)(var_x)
 </s> add             var_x = kl.GaussianNoise(1.0)(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 = Reshape(new_shape)(var_x)
 </s> add             var_x = kl.Reshape(new_shape)(var_x)
            var_x = kl.Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x)
              var_x = Dense(nodes)(var_x)
  
          if self._side != "gblock":
              dim = self._config["fc_dimensions"]
            var_x = kl.Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x)
              var_x = self._do_upsampling(var_x)
  
              num_upscales = self._config["dec_upscales_in_fc"]
              if num_upscales:
                  var_x = UpscaleBlocks(self._side,
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