          outputs = [decoders["a"], decoders["b"]]
          autoencoder = KModel(inputs, outputs, name=self.model_name)
          return autoencoder
  
      def _build_encoders(self, inputs: list[Tensor]) -> dict[str, keras.models.Model]:
          """ Build the encoders for Phaze-A
  
          Parameters
          ----------
          inputs: list
 
</s> remove         autoencoder = KModel(inputs, outputs, name=self.model_name)
 </s> add         autoencoder = keras.models.Model(inputs, outputs, name=self.model_name) </s> remove                         inputs: dict[str, list[keras.models.Model] | keras.models.Model]
                        ) -> dict[str, keras.models.Model]:
 </s> add                         inputs: dict[str, list[tf.keras.models.Model] | tf.keras.models.Model]
                        ) -> dict[str, tf.keras.models.Model]: </s> remove             inputs: dict[str, keras.models.Model]) -> dict[str, list[keras.models.Model]]:
 </s> add             inputs: dict[str, tf.keras.models.Model]) -> dict[str, list[tf.keras.models.Model]]: </s> remove                 inputs: dict[str, list[keras.models.Model]]
            ) -> dict[str, list[keras.models.Model] | keras.models.Model]:
 </s> add                 inputs: dict[str, list[tf.keras.models.Model]]
            ) -> dict[str, list[tf.keras.models.Model] | tf.keras.models.Model]: </s> remove     def build_model(self, inputs: list[Tensor]) -> keras.models.Model:
 </s> add     def build_model(self, inputs: list[tf.Tensor]) -> tf.keras.models.Model:
    def _build_encoders(self, inputs: list[tf.Tensor]) -> dict[str, tf.keras.models.Model]:
          outputs = [decoders["a"], decoders["b"]]
          autoencoder = KModel(inputs, outputs, name=self.model_name)
          return autoencoder
  
    def _build_encoders(self, inputs: list[tf.Tensor]) -> dict[str, tf.keras.models.Model]:
          """ Build the encoders for Phaze-A
  
          Parameters
          ----------
          inputs: list
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