code: def evaluate_audio_reconstruction(
      y_pred: torch.Tensor, y: torch.Tensor, cfg: omegaconf.DictConfig
  ) -> dict:
      """Audio reconstruction evaluation method that can be conveniently pickled."""
      metrics = {}
      if cfg.evaluate.metrics.visqol:
          visqol = builders.get_visqol(cfg.metrics.visqol)
          metrics["visqol"] = visqol(y_pred, y, cfg.sample_rate)
      sisnr = builders.get_loss("sisnr", cfg)
      metrics["sisnr"] = sisnr(y_pred, y)
      return metrics
  
imports: import logging
  import multiprocessing
  from pathlib import Path
  import typing
  import flashy
  import omegaconf
  import torch
  from torch import nn
  from .None import base, builders
  from ..None import models, quantization
  from ..utils import checkpoint
  from ..utils.samples.manager import SampleManager
  from ..utils.utils import get_pool_executor
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate_audio_reconstruction(y_pred, y, cfg) and returns: metrics