          retval = dict(a=encoder(inputs[0]), b=encoder(inputs[1]))
          logger.debug("Encoders: %s", retval)
          return retval
  
      def _build_fully_connected(self, inputs):
          """ Build the fully connected layers for Phaze-A
  
          Parameters
          ----------
          inputs: dict
 
</s> remove     def _build_decoders(self, inputs):
 </s> add     def _build_decoders(
            self,
            inputs: Dict[str, Union[List[keras.models.Model], keras.models.Model]]
            ) -> Dict[str, keras.models.Model]: </s> remove     def _build_g_blocks(self, inputs):
 </s> add     def _build_g_blocks(
                self,
                inputs: Dict[str, List[keras.models.Model]]
            ) -> Dict[str, Union[List[keras.models.Model], keras.models.Model]]: </s> remove     def _build_encoders(self, inputs):
 </s> add     def _build_encoders(self, inputs: List[Tensor]) -> Dict[str, keras.models.Model]: </s> remove def _bottleneck(inputs, bottleneck, size, normalization):
 </s> add def _bottleneck(inputs: Tensor, bottleneck: str, size: int, normalization: str) -> Tensor: </s> remove     def __call__(self):
 </s> add     def _do_upsampling(self, inputs: Tensor) -> Tensor:
        """ Perform the upsampling at the end of the fully connected layers.

        Parameters
        ----------
        inputs: Tensor
            The input to the upsample layers

        Returns
        -------
        Tensor
            The output from the upsample layers
        """
        upsample_filts = self._scale_filters(self._config["fc_upsample_filters"])
        upsampler = self._config["fc_upsampler"].lower()
        num_upsamples = self._config["fc_upsamples"]
        var_x = inputs
        if upsampler == "upsample2d" and num_upsamples > 1:
            upscaler = _get_upscale_layer(upsampler,
                                          upsample_filts,  # Not used but required
                                          upsamples=2 ** num_upsamples,
                                          interpolation="bilinear")
            var_x = upscaler(var_x)
        else:
            for _ in range(num_upsamples):
                upscaler = _get_upscale_layer(upsampler,
                                              upsample_filts,
                                              activation="leakyrelu")
                var_x = upscaler(var_x)
        if upsampler == "upsample2d":
            var_x = LeakyReLU(alpha=0.1)(var_x)
        return var_x

    def __call__(self) -> keras.models.Model:
    def _build_fully_connected(
            self,
            inputs: Dict[str, keras.models.Model]) -> Dict[str, List[keras.models.Model]]:
          retval = dict(a=encoder(inputs[0]), b=encoder(inputs[1]))
          logger.debug("Encoders: %s", retval)
          return retval
  
    def _build_fully_connected(
            self,
            inputs: Dict[str, keras.models.Model]) -> Dict[str, List[keras.models.Model]]:
          """ Build the fully connected layers for Phaze-A
  
          Parameters
          ----------
          inputs: dict
keep keep keep keep replace keep keep keep keep keep
 Model updates
- Increase model summary width
- Phaze A updates
  - Update some min/max values
  - Add Decoder Filter Slope Mode
  - Add additional arguments for Upsampling2D
  - Adjust upsampling method for multiple upsamples in FC layers
  - Typing 
plugins/train/model/phaze_a.py