code: def multi_evaluation_epoch_end(
      self, outputs, dataloader_idx: int = 0, tag: str = "val"
  ):
      # Handle loss
      loss_mean = torch.stack(
          [x[f"{tag}_loss"] for x in outputs]
      ).mean()
      tensorboard_logs = {f"{tag}_loss": loss_mean}
  
      # Handle metrics for this tag and dataloader_idx
      if hasattr(self, "metrics") and tag in self.metrics:
          for name, metric in self.metrics[tag][dataloader_idx].items():
              # Compute & reset the metric
              value = metric.compute()
              metric.reset()
              # Store for logs
              tensorboard_logs[f"{tag}_{name}"] = value
  
      return {f"{tag}_loss": loss_mean, "log": tensorboard_logs}
  
imports: from abc import ABC, abstractmethod
  from typing import List, Union
  import hydra
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from nemo.collections.asr.metrics.audio import AudioMetricWrapper
  from nemo.core.classes import ModelPT
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 10 for calling the AudioToAudioModel obj's underlying member method code with example usage: obj.multi_evaluation_epoch_end(outputs, dataloader_idx, tag) and returns: dict