code: def dataio_prep(params):
      "Creates the dataloaders and their data processing pipelines."
  
      data_folder = params["data_folder"]
  
      # 1. Declarations:
  
      # Train data (used for normalization)
      train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
          csv_path=params["train_data"],
          replacements={"data_root": data_folder},
      )
      train_data = train_data.filtered_sorted(
          sort_key="duration", select_n=params["n_train_snts"]
      )
  
      # Enrol data
      enrol_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
          csv_path=params["enrol_data"],
          replacements={"data_root": data_folder},
      )
      enrol_data = enrol_data.filtered_sorted(sort_key="duration")
  
      # Test data
      test_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
          csv_path=params["test_data"],
          replacements={"data_root": data_folder},
      )
      test_data = test_data.filtered_sorted(sort_key="duration")
  
      datasets = [train_data, enrol_data, test_data]
  
      # 2. Define audio pipeline:
      @sb.utils.data_pipeline.takes("wav", "start", "stop")
      @sb.utils.data_pipeline.provides("sig")
      def audio_pipeline(wav, start, stop):
          start = int(start)
          stop = int(stop)
          num_frames = stop - start
          sig, fs = torchaudio.load(
              wav, num_frames=num_frames, frame_offset=start
          )
          sig = sig.transpose(0, 1).squeeze(1)
          return sig
  
      sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
  
      # 3. Set output:
      sb.dataio.dataset.set_output_keys(
          datasets, ["id", "sig", "spk_id"]
      )
  
      # 4 Create dataloaders
      train_dataloader = sb.dataio.dataloader.make_dataloader(
          train_data, **params["train_dataloader_opts"]
      )
      enrol_dataloader = sb.dataio.dataloader.make_dataloader(
          enrol_data, **params["enrol_dataloader_opts"]
      )
      test_dataloader = sb.dataio.dataloader.make_dataloader(
          test_data, **params["test_dataloader_opts"]
      )
  
      return train_dataloader, enrol_dataloader, test_dataloader
  
imports: import os
  import sys
  import torch
  import torchaudio
  import logging
  import speechbrain
  import numpy
  import pickle
  from tqdm.contrib import tqdm
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import EER, minDCF
  from speechbrain.processing.PLDA_LDA import StatObject_SB
  from speechbrain.processing.PLDA_LDA import Ndx
  from speechbrain.processing.PLDA_LDA import fast_PLDA_scoring
  from speechbrain.utils.data_utils import download_file
  from speechbrain.utils.distributed import run_on_main
label_desc: Use image node_id 5 for calling a global function with example usage: dataio_prep(params) and returns: train_dataloader,  enrol_dataloader,  test_dataloader, sig