                        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:]
          encoder_a = encoder(inputs[0])
          encoder_b = encoder(inputs[1])
  
          if self.architecture == "liae":
 
</s> remove         model["kwargs"] = self._model_kwargs.get(arch, dict())
 </s> add         model["kwargs"] = self._model_kwargs.get(arch, {}) </s> remove             valid = [x for x in _MODEL_MAPPING if not _MODEL_MAPPING[x].get('no_amd')]
 </s> add             valid = [k for k, v in _MODEL_MAPPING.items() if not v.get('no_amd')] </s> remove         self._name = "conv{}_".format(level)
 </s> add         self._name = f"conv{level}_" </s> remove         self._name = "conv{}_".format(level)
 </s> add         self._name = f"conv{level}_" </s> remove         ret = list()
 </s> add         ret = []
        encoder = getattr(self, f"encoder_{self.architecture}")()
                        if k in ("freeze_layers", "load_layers")})
  
      def build_model(self, inputs):
          """ Build the DFL-SAE Model """
        encoder = getattr(self, f"encoder_{self.architecture}")()
          enc_output_shape = encoder.output_shape[1:]
          encoder_a = encoder(inputs[0])
          encoder_b = encoder(inputs[1])
  
          if self.architecture == "liae":
keep keep keep keep replace keep keep keep keep keep
 Update all Keras Imports to be conditional (#1214)

* Remove custom keras importer

* first round keras imports fix

* launcher.py: Remove KerasFinder references

* 2nd round keras imports update (lib and extract)

* 3rd round keras imports update (train)

* remove KerasFinder from tests

* 4th round keras imports update (tests) 
plugins/train/model/dfl_sae.py