              size = self._get_smallest_size(size // 2, idx - 1)
          return size
  
  
  class LossWrapper():
      """ A wrapper class for multiple keras losses to enable multiple masked weighted loss
      functions on a single output.
  
      Notes
      -----
 
</s> remove     def __call__(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
 </s> add     def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor: </s> remove     def save(self) -> None:
 </s> add     def save(self, is_exit: bool = False) -> None: </s> remove     def _process_gui_triggers(self):
 </s> add     def _process_gui_triggers(self) -> None: </s> remove     def save(self) -> None:
 </s> add     def save(self, is_exit: bool = False) -> None:
class LossWrapper(tf.keras.losses.Loss):
              size = self._get_smallest_size(size // 2, idx - 1)
          return size
  
  
class LossWrapper(tf.keras.losses.Loss):
      """ A wrapper class for multiple keras losses to enable multiple masked weighted loss
      functions on a single output.
  
      Notes
      -----
keep keep keep keep replace keep keep keep keep keep
 Training: Add setting option to save optimizer weights 
lib/model/loss/loss_tf.py