              outputs = [self.decoder("a", enc_output_shape)(encoder_a),
                         self.decoder("b", enc_output_shape)(encoder_b)]
          autoencoder = KerasModel(inputs,
                                   outputs,
                                   name="{}_{}".format(self.name, self.architecture))
          return autoencoder
  
      def encoder_df(self):
          """ DFL SAE DF Encoder Network"""
          input_ = Input(shape=self.input_shape)
 
</s> remove         self.architecture = self.config["architecture"].lower()
 </s> add  </s> remove         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> add         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> remove         The parent plugin class that owns the IO functions.
 </s> add         The parent plugin class that owns the weights functions.
                                 name=self.name)
              outputs = [self.decoder("a", enc_output_shape)(encoder_a),
                         self.decoder("b", enc_output_shape)(encoder_b)]
          autoencoder = KerasModel(inputs,
                                   outputs,
                                 name=self.name)
          return autoencoder
  
      def encoder_df(self):
          """ DFL SAE DF Encoder Network"""
          input_ = Input(shape=self.input_shape)
keep keep keep keep replace keep keep keep keep keep
 Bugfix -Weights freezing/loading for dfl-sae 
plugins/train/model/dfl_sae.py