          return KerasModel(input_, outputs=outputs, name="decoder_{}".format(side))
  
      def _legacy_mapping(self):
          """ The mapping of legacy separate model names to single model names """
          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"})
          return mappings[self.config["architecture"]]
 
</s> remove         The parent plugin class that owns the IO functions.
 </s> add         The parent plugin class that owns the weights functions. </s> remove         self.architecture = self.config["architecture"].lower()
 </s> add  </s> remove                                  name="{}_{}".format(self.name, self.architecture))
 </s> add                                  name=self.name)
        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"})
          return KerasModel(input_, outputs=outputs, name="decoder_{}".format(side))
  
      def _legacy_mapping(self):
          """ The mapping of legacy separate model names to single model names """
        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"})
        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"})
        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"})
        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"})
        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"})
        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"})
        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"})
          return mappings[self.config["architecture"]]
keep keep keep keep replace replace replace replace replace replace replace keep
 Bugfix -Weights freezing/loading for dfl-sae 
plugins/train/model/dfl_sae.py