          if self._backend == "amd" and batch_size is not None:
              return self._amd_predict_with_optimized_batchsizes(feed, batch_size)
          return self._model.predict(feed, batch_size=batch_size)
  
      def _amd_predict_with_optimized_batchsizes(self, feed, batch_size):
          """ Minimizes the amount of kernels to be compiled when using the ``amd`` backend with
          varying batch sizes while trying to keep the batchsize as high as possible.
  
          Parameters
          ----------
 
</s> remove         return self._model.predict(feed, batch_size=batch_size)
 </s> add         return self._model.predict(feed, verbose=0, batch_size=batch_size) </s> remove     def _setup_amd(cls, arguments):
 </s> add     def _setup_amd(cls, arguments: "argparse.Namespace") -> bool: </s> remove     def predict(self, feed, batch_size=None):
 </s> add     def predict(self,
                feed: Union[List[np.ndarray], np.ndarray],
                batch_size: Optional[int] = None) -> Union[List[np.ndarray], np.ndarray]:
    def _amd_predict_with_optimized_batchsizes(
            self,
            feed: Union[List[np.ndarray], np.ndarray],
            batch_size: int) -> Union[List[np.ndarray], np.ndarray]:
          if self._backend == "amd" and batch_size is not None:
              return self._amd_predict_with_optimized_batchsizes(feed, batch_size)
          return self._model.predict(feed, batch_size=batch_size)
  
    def _amd_predict_with_optimized_batchsizes(
            self,
            feed: Union[List[np.ndarray], np.ndarray],
            batch_size: int) -> Union[List[np.ndarray], np.ndarray]:
          """ Minimizes the amount of kernels to be compiled when using the ``amd`` backend with
          varying batch sizes while trying to keep the batchsize as high as possible.
  
          Parameters
          ----------
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