code: def dataio_prep(hparams, train_step=None):
      """This function prepares the datasets to be used in the brain class.
      It also defines the data processing pipeline through user-defined functions.
  
      Arguments
      ---------
      hparams: dict
          the hyperparameters dictionary
  
      train_step: dict
          the hyperparameters for the training step being executed
  
      Returns
      -------
      train_data: speechbrain.dataio.dataset.DynamicItemDataset
          the training dataset
  
      valid_data: speechbrain.dataio.dataset.DynamicItemDataset
          the validation dataset
  
      test_data: speechbrain.dataio.dataset.DynamicItemDataset
          the test dataset
  
      phoneme_encoder: speechbrain.dataio.encoder.TextEncoder
          the phoneme encoder
      """
  
      if not train_step:
          train_step = hparams
  
      # 1. Load the datasets:
      train_data, valid_data, test_data = load_datasets(
          hparams, train_step
      )
  
      if hparams["sorting"] == "ascending":
          # when sorting do not shuffle in dataloader ! otherwise is pointless
          hparams["dataloader_opts"]["shuffle"] = False
  
      elif hparams["sorting"] == "descending":
          hparams["dataloader_opts"]["shuffle"] = False
  
      elif hparams["sorting"] == "random":
          pass
  
      else:
          raise NotImplementedError(
              "sorting must be random, ascending or descending"
          )
      is_homograph = (
          TrainMode(train_step.get("mode", TrainMode.NORMAL))
          == TrainMode.HOMOGRAPH
      )
  
      train_data = sort_data(train_data, hparams, train_step)
      valid_data = sort_data(valid_data, hparams, train_step)
      test_data = sort_data(test_data, hparams, train_step)
  
      datasets = [train_data, valid_data, test_data]
  
      phoneme_encoder = hparams["phoneme_encoder"]
      grapheme_encoder = hparams["grapheme_encoder"]
  
      # 2. Define grapheme and phoneme pipelines:
      enable_eos_bos(
          tokens=hparams["phonemes"],
          encoder=phoneme_encoder,
          bos_index=hparams["bos_index"],
          eos_index=hparams["eos_index"],
      )
      enable_eos_bos(
          tokens=hparams["graphemes"],
          encoder=grapheme_encoder,
          bos_index=hparams["bos_index"],
          eos_index=hparams["eos_index"],
      )
      if hparams.get("char_tokenize"):
          grapheme_pipeline_item = partial(
              tokenizer_encode_pipeline,
              tokenizer=hparams["grapheme_tokenizer"],
              tokens=hparams["graphemes"],
              wordwise=hparams["char_token_wordwise"],
              token_space_index=hparams["token_space_index"],
          )
      else:
          grapheme_pipeline_item = partial(
              grapheme_pipeline, grapheme_encoder=grapheme_encoder
          )
  
      if hparams.get("phn_tokenize"):
          phoneme_pipeline_item = partial(
              tokenizer_encode_pipeline,
              tokenizer=hparams["phoneme_tokenizer"],
              tokens=hparams["phonemes"],
              char_map=hparams["phn_char_map"],
              wordwise=hparams["phn_token_wordwise"],
              token_space_index=hparams["token_space_index"],
          )
          # Ensure the tokenizers are trained
          if "grapheme_tokenizer" in hparams:
              hparams["grapheme_tokenizer"]()
          if "phoneme_tokenizer" in hparams:
              hparams["phoneme_tokenizer"]()
          enable_eos_bos(
              tokens=hparams["phonemes"],
              encoder=phoneme_encoder,
              bos_index=hparams["bos_index"],
              eos_index=hparams["eos_index"],
          )
      else:
          phoneme_pipeline_item = partial(
              phoneme_pipeline,
              phoneme_encoder=phoneme_encoder,
          )
  
      phn_bos_eos_pipeline_item = partial(
          add_bos_eos, encoder=phoneme_encoder
      )
      grapheme_bos_eos_pipeline_item = partial(
          add_bos_eos,
          # TODO: Use the grapheme encoder here (this will break some models)
          encoder=phoneme_encoder,
      )
  
      dynamic_items = [
          {
              "func": grapheme_pipeline_item,
              "takes": ["char"],
              "provides": [
                  "grapheme_list",
                  "grpaheme_encoded_list",
                  "grapheme_encoded",
              ],
          },
          {
              "func": phoneme_pipeline_item,
              "takes": ["phn"],
              "provides": [
                  "phn_list",
                  "phn_encoded_list",
                  "phn_encoded",
              ],
          },
          {
              "func": phn_bos_eos_pipeline_item,
              "takes": ["phn_encoded"],
              "provides": [
                  "phn_encoded_bos",
                  "phn_len_bos",
                  "phn_encoded_eos",
                  "phn_len_eos",
              ],
          },
          {
              "func": grapheme_bos_eos_pipeline_item,
              "takes": ["grapheme_encoded"],
              "provides": [
                  "grapheme_encoded_bos",
                  "grapheme_len_bos",
                  "grapheme_encoded_eos",
                  "grapheme_len_eos",
              ],
          },
      ]
  
      if hparams.get("phn_tokenize"):
          # A raw tokenizer is needed to determine the correct
          # word boundaries from data
          phoneme_raw_pipeline = partial(
              phoneme_pipeline,
              phoneme_encoder=phoneme_encoder,
          )
          dynamic_items.append(
              {
                  "func": phoneme_raw_pipeline,
                  "takes": ["phn"],
                  "provides": [
                      "phn_raw_list",
                      "phn_raw_encoded_list",
                      "phn_raw_encoded",
                  ],
              }
          )
      for dynamic_item in dynamic_items:
          sb.dataio.dataset.add_dynamic_item(datasets, **dynamic_item)
  
      # 3. Set output:
      output_keys = [
          "sample_id",
          "grapheme_encoded",
          "grapheme_encoded_bos",
          "grapheme_encoded_eos",
          "phn_encoded",
          "phn_encoded_eos",
          "phn_encoded_bos",
      ]
      if is_homograph:
          output_keys += [
              "homograph_wordid",
              "homograph_phn_start",
              "homograph_phn_end",
          ]
  
      if hparams.get("use_word_emb", False):
          output_keys.append("char")
      if (
          hparams.get("phn_tokenize", False)
          and "phn_raw_encoded" not in output_keys
      ):
          output_keys.append("phn_raw_encoded")
  
      sb.dataio.dataset.set_output_keys(
          datasets,
          output_keys,
      )
      if "origins" in hparams:
          datasets = [
              filter_origins(dataset, hparams) for dataset in datasets
          ]
  
      if train_step.get("mode") == "homograph":
          datasets = [
              filter_homograph_positions(dataset)
              for dataset in datasets
          ]
  
      train_data, valid_data, test_data = datasets
      return train_data, valid_data, test_data, phoneme_encoder
  
imports: from speechbrain.dataio.dataset import FilteredSortedDynamicItemDataset, DynamicItemDataset
  from speechbrain.dataio.sampler import BalancingDataSampler
  from speechbrain.utils.data_utils import undo_padding
  import datasets
  import logging
  import os
  import random
  import speechbrain
  import sys
  from enum import Enum
  from collections import namedtuple
  from hyperpyyaml import load_hyperpyyaml
  from functools import partial
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.utils.pretrained import save_for_pretrained
  from speechbrain.lobes.models.g2p.dataio import enable_eos_bos, grapheme_pipeline, phoneme_pipeline, tokenizer_encode_pipeline, add_bos_eos, get_sequence_key, phonemes_to_label
  from speechbrain.dataio.wer import print_alignments
  from speechbrain.wordemb.util import expand_to_chars
  from io import StringIO
  from speechbrain.utils import hpopt
  import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: dataio_prep(hparams, train_step) and returns: train_data,  valid_data,  test_data,  phoneme_encoder