          if retval == 0:
              retval = 256 if self.architecture == "liae" else 512
          return retval
  
      def _patch_weights_management(self, arguments):
          """ Patch in the correct encoder name into the config dictionary for freezing and loading
          weights based on architecture.
  
          Because of variable model name based on architecture, configfile needs to be loaded
          prior to initializing parent
  
          Parameters
          ----------
          arguments: :class:`argparse.Namespace`
              The arguments that were passed to the train or convert process as generated from
              Faceswap's command line arguments
  
          """
          self._configfile = arguments.configfile if hasattr(arguments, "configfile") else None
          self.architecture = self.config["architecture"].lower()
          self.config["freeze_layers"] = [f"encoder_{self.architecture}"]
          self.config["load_layers"] = [f"encoder_{self.architecture}"]
          logger.debug("Patched encoder layers to config: %s", self.config)
  
      def build_model(self, inputs):
          """ Build the DFL-SAE Model """
          encoder = getattr(self, "encoder_{}".format(self.architecture))()
          enc_output_shape = encoder.output_shape[1:]
          encoder_a = encoder(inputs[0])
 
</s> remove     @property
    def name(self):
        """ str: The name of this model based on the plugin name. Overridden as DFL-SAE is
        named differently depending on the architecture selected. """
        basename = super().name
        name = f"{basename}_{self.architecture}"
        return name

 </s> add  </s> remove         The parent plugin class that owns the weights functions.
 </s> add         The parent plugin class that owns the IO functions. </s> remove         name = "dfl_sae"
        mappings = dict(df={"{}_encoder.h5".format(name): "encoder_df",
                            "{}_decoder_A.h5".format(name): "decoder_a",
                            "{}_decoder_B.h5".format(name): "decoder_b"},
                        liae={"{}_encoder.h5".format(name): "encoder_liae",
                              "{}_intermediate_B.h5".format(name): "intermediate_both",
                              "{}_intermediate.h5".format(name): "intermediate_b",
                              "{}_decoder.h5".format(name): "decoder_both"})
 </s> add         mappings = dict(df={"{}_encoder.h5".format(self.name): "encoder_df",
                            "{}_decoder_A.h5".format(self.name): "decoder_a",
                            "{}_decoder_B.h5".format(self.name): "decoder_b"},
                        liae={"{}_encoder.h5".format(self.name): "encoder_liae",
                              "{}_intermediate_B.h5".format(self.name): "intermediate_both",
                              "{}_intermediate.h5".format(self.name): "intermediate_b",
                              "{}_decoder.h5".format(self.name): "decoder_both"}) </s> remove 
        self._patch_weights_management(args[1])

 </s> add 
          if retval == 0:
              retval = 256 if self.architecture == "liae" else 512
          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:]
          encoder_a = encoder(inputs[0])
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Revert "Bugfix -Weights freezing/loading for dfl-sae"

This reverts commit 086933d97dbd50bb9291026c1cf90fe94f463ce1. 
plugins/train/model/dfl_sae.py