code: def get_evaluation_dataloader(config):
      """Prepare a dataloader for evaluation."""
      dataset = audio_to_audio_dataset.get_audio_to_target_dataset(
          config=config
      )
  
      return torch.utils.data.DataLoader(
          dataset=dataset,
          batch_size=config["batch_size"],
          collate_fn=dataset.collate_fn,
          drop_last=config.get("drop_last", False),
          shuffle=False,
          num_workers=config.get(
              "num_workers",
              min(config["batch_size"], os.cpu_count() - 1),
          ),
          pin_memory=True,
      )
  
imports: import json
  import os
  import tempfile
  from dataclasses import dataclass, field, is_dataclass
  from typing import List, Optional
  import process_audio
  import torch
  from omegaconf import OmegaConf, open_dict
  from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality
  from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio, SignalDistortionRatio
  from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
  from tqdm import tqdm
  from nemo.collections.asr.data import audio_to_audio_dataset
  from nemo.collections.asr.metrics.audio import AudioMetricWrapper
  from nemo.collections.common.parts.preprocessing import manifest
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: get_evaluation_dataloader(config) and returns: torch