          self._model.load_weights(self._model_path)
          if self._backend != "amd":
              self._model.make_predict_function()
  
      def append_softmax_activation(self, layer_index=-1):
          """ Append a softmax activation layer to a model
  
          Occasionally a softmax activation layer needs to be added to a model's output.
          This is a convenience function to append this layer to the loaded model.
  
 
</s> remove     def load_model_weights(self):
 </s> add     def load_model_weights(self) -> None: </s> remove     def define_model(self, function):
 </s> add     def define_model(self, function: Callable) -> None: </s> remove     def load_model(self):
 </s> add     def load_model(self) -> None: </s> remove         logger.verbose("Initializing plugin model: %s", self._name)
 </s> add         logger.verbose("Initializing plugin model: %s", self._name)  # type:ignore
        assert self._model is not None
    def append_softmax_activation(self, layer_index: int = -1) -> None:
          self._model.load_weights(self._model_path)
          if self._backend != "amd":
              self._model.make_predict_function()
  
    def append_softmax_activation(self, layer_index: int = -1) -> None:
          """ Append a softmax activation layer to a model
  
          Occasionally a softmax activation layer needs to be added to a model's output.
          This is a convenience function to append this layer to the loaded model.
  
keep keep keep keep replace keep keep keep keep keep
 Update TF to 2.9
    - Update TF to 2.7 to 2.9
    - Bump dependencies
    - Remove decode from pynvml calls
    - force keras predict functions to non-verbose
    - update tests
    - update Tensorboard logging
    - Update docs 
lib/model/session.py