          return retval
  
      def build_model(self, inputs):
          """ Build the DFL-SAE Model """
          encoder = getattr(self, "encoder_{}".format(self.architecture))()
          enc_output_shape = encoder.output_shape[1:]
 
</s> remove         autoencoder = KerasModel(inputs, outputs, name=self.name)
 </s> add         autoencoder = KerasModel(inputs, outputs, name=self.model_name) </s> remove         autoencoder = KerasModel(inputs, outputs, name=self.name)
 </s> add         autoencoder = KerasModel(inputs, outputs, name=self.model_name) </s> remove         autoencoder = KerasModel(inputs, outputs, name=self.name)
 </s> add         autoencoder = KerasModel(inputs, outputs, name=self.model_name) </s> remove         autoencoder = KerasModel(inputs, outputs, name=self.name)
 </s> add         autoencoder = KerasModel(inputs, outputs, name=self.model_name)
    def _patch_weights_management(self):
        """ Patch in the correct encoder name into the config dictionary for freezing and loading
        weights based on architecture.
        """
        self.config["freeze_layers"] = [f"encoder_{self.architecture}"]
        self.config["load_layers"] = [f"encoder_{self.architecture}"]
        logger.debug("Patched encoder layers to config: %s",
                     {k: v for k, v in self.config.items()
                      if k in ("freeze_layers", "load_layers")})

          return retval
  
    def _patch_weights_management(self):
        """ Patch in the correct encoder name into the config dictionary for freezing and loading
        weights based on architecture.
        """
        self.config["freeze_layers"] = [f"encoder_{self.architecture}"]
        self.config["load_layers"] = [f"encoder_{self.architecture}"]
        logger.debug("Patched encoder layers to config: %s",
                     {k: v for k, v in self.config.items()
                      if k in ("freeze_layers", "load_layers")})

      def build_model(self, inputs):
          """ Build the DFL-SAE Model """
          encoder = getattr(self, "encoder_{}".format(self.architecture))()
          enc_output_shape = encoder.output_shape[1:]
keep add keep keep keep keep
 Bugfix -Weights freezing/loading for dfl-sae 
plugins/train/model/dfl_sae.py