              outputs.append(Conv2DOutput(1,
                                          self._config["dec_output_kernel"],
                                          name="mask_out")(var_y))
  
          return KModel(inputs, outputs=outputs, name=f"decoder_{self._side}")
 
</s> remove     def _build_encoders(self, inputs: list[Tensor]) -> dict[str, keras.models.Model]:
 </s> add     def _build_encoders(self, inputs: list[tf.Tensor]) -> dict[str, tf.keras.models.Model]: </s> remove         autoencoder = KModel(inputs, outputs, name=self.model_name)
 </s> add         autoencoder = keras.models.Model(inputs, outputs, name=self.model_name) </s> remove         return UpSampling2D(**kwargs)
 </s> add         return kl.UpSampling2D(**kwargs) </s> remove         return KModel(input_, var_x, name="encoder")
 </s> add         return keras.models.Model(input_, var_x, name="encoder") </s> remove         return KModel(input_, var_x, name=f"fc_{self._side}")
 </s> add         return keras.models.Model(input_, var_x, name=f"fc_{self._side}")
        return keras.models.Model(inputs, outputs=outputs, name=f"decoder_{self._side}")
              outputs.append(Conv2DOutput(1,
                                          self._config["dec_output_kernel"],
                                          name="mask_out")(var_y))
  
        return keras.models.Model(inputs, outputs=outputs, name=f"decoder_{self._side}")
keep keep keep keep replace
 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