code: def _get_result_for_report(self):
      stage = "val"
      if not self.trainer.val_dataloaders:
          _logger.debug(
              "No validation dataloader. Use results on training set instead."
          )
          stage = "train"
  
      if len(self.metrics) == 1:
          metric_name = next(iter(self.metrics))
          return self.trainer.callback_metrics[
              f"{stage}_{metric_name}"
          ].item()
      else:
          warnings.warn(
              'Multiple metrics without "default" is not supported by current framework.'
          )
          return {
              name: self.trainer.callback_metrics[
                  f"{stage}_{name}"
              ].item()
              for name in self.metrics
          }
  
imports: import logging
  import os
  import warnings
  from pathlib import Path
  from typing import Any, Dict, Union, Optional, List, Type
  import pytorch_lightning
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchmetrics
  import torchmetrics.classification
  import torch.utils.data
  import nni
  from nni.common.serializer import is_traceable
  from nni.nas.evaluator import MutableEvaluator
label_desc: Use image node_id 11 for calling the SupervisedLearningModule obj's underlying member method code with example usage: obj._get_result_for_report() and returns: self, unknown