          -------
          :class:`numpy.ndarray`
              The predictions from the model
          """
          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, verbose=0, batch_size=batch_size)
  
      def _amd_predict_with_optimized_batchsizes(
              self,
 
</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 _amd_predict_with_optimized_batchsizes(self, feed, batch_size):
 </s> add     def _amd_predict_with_optimized_batchsizes(
            self,
            feed: Union[List[np.ndarray], np.ndarray],
            batch_size: int) -> Union[List[np.ndarray], np.ndarray]: </s> remove             logger.verbose("Hiding GPUs from Tensorflow")
 </s> add             logger.verbose("Hiding GPUs from Tensorflow")  # type:ignore </s> remove             ``None`` to not exclude any GPUs. Default: ``None``
 </s> add             ``None`` to not exclude any GPUs
        assert self._model is not None
          -------
          :class:`numpy.ndarray`
              The predictions from the model
          """
        assert self._model is not None
          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, verbose=0, batch_size=batch_size)
  
      def _amd_predict_with_optimized_batchsizes(
              self,
keep keep keep add keep 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