code: def dataio_prep(hparams):
      """This function prepares the datasets to be used in the brain class."""
  
      # Define datasets
      datasets = {}
      data_info = {
          "train": hparams["train_annotation"],
          "valid": hparams["valid_annotation"],
          "test": hparams["test_annotation"],
      }
      for dataset in data_info:
          datasets[
              dataset
          ] = sb.dataio.dataset.DynamicItemDataset.from_json(
              json_path=data_info[dataset],
              replacements={"data_root": hparams["data_folder"]},
              dynamic_items=[audio_pipeline],
              output_keys=["id", "noisy_sig", "clean_sig", "clean_wav"],
          )
  
      return datasets
  
imports: import os
  import sys
  import shutil
  import pickle
  import torch
  import torchaudio
  import speechbrain
  from pesq import pesq
  from enum import Enum, auto
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import MetricStats
  from speechbrain.processing.features import spectral_magnitude
  from speechbrain.nnet.loss.stoi_loss import stoi_loss
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.dataio.sampler import ReproducibleWeightedRandomSampler
label_desc: Use image node_id 4 for calling a global function with example usage: dataio_prep(hparams) and returns: datasets