code: def dataio_prepare(hparams):
      """This function prepares the datasets to be used in the brain class.
      It also defines the data processing pipeline through user-defined functions.
      """
      segment_size = hparams["segment_size"]
  
      # Define audio pipeline:
      @sb.utils.data_pipeline.takes("wav", "segment")
      @sb.utils.data_pipeline.provides("mel", "sig")
      def audio_pipeline(wav, segment):
          audio = sb.dataio.dataio.read_audio(wav)
          audio = torch.FloatTensor(audio)
          audio = audio.unsqueeze(0)
          if segment:
              if audio.size(1) >= segment_size:
                  max_audio_start = audio.size(1) - segment_size
                  audio_start = torch.randint(0, max_audio_start, (1,))
                  audio = audio[
                      :, audio_start : audio_start + segment_size
                  ]
              else:
                  audio = torch.nn.functional.pad(
                      audio,
                      (0, segment_size - audio.size(1)),
                      "constant",
                  )
  
          mel = hparams["mel_spectogram"](audio=audio.squeeze(0))
  
          return mel, audio
  
      datasets = {}
      data_info = {
          "train": hparams["train_json"],
          "valid": hparams["valid_json"],
          "test": hparams["test_json"],
      }
      for dataset in hparams["splits"]:
          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", "mel", "sig"],
          )
  
      return datasets
  
imports: import sys
  import torch
  import copy
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  from speechbrain.utils.data_utils import scalarize
  import torchaudio
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: dataio_prepare(hparams) and returns: datasets, mel,  audio