code: def update_batch(self, loss_per_instance):
          
          if self.batch_indices is None:
              raise TensorForceError("Need to call get_batch before each update_batch call.")
          # if len(loss_per_instance) != len(self.batch_indices):
          #     raise TensorForceError("For all instances a loss value has to be provided.")
  
          for index, loss in zip(self.batch_indices, loss_per_instance):
              # Sampling priority is proportional to the largest absolute temporal difference error.
              new_priority = (np.abs(loss) + self.prioritization_constant) ** self.prioritization_weight
              self.observations._move(index, new_priority)
              self.none_priority_index += 1
docs: Computes priorities according to loss.
  
          Args:
              loss_per_instance: