def METHOD_NAME(self, current):
    # If we don't have enough models
    if len(self.best_k_models) < self.save_top_k:
        return True
    # Convert to torch if necessary
    if not isinstance(current, torch.Tensor):
        current = torch.tensor(current)
    # Get monitoring operation
    monitor_op = {
        'min': torch.lt,
        'max': torch.gt,
    }[self.mode]
    # Compare and return
    return monitor_op(current, self.best_k_models[self.kth_best_model])