code: def enhance_with_random(dataset, hparams):
      """Enhances the pipeline with an additional randomly chosen sample for
      each sample - used for training the Done detector - to determine word
      boundaries
  
      Arguments
      ---------
      dataset: DynamicItemDataset
          the dataset to be enhanced
      hparams: dict
          the hyperparameters dictionary
      """
      item_count = len(dataset)
  
      @sb.utils.data_pipeline.provides("file_name_random", "sig_random")
      def extra_random_sample():
          idx = torch.randint(item_count, (1,)).item()
          data_id = dataset.data_ids[idx]
          data_item = dataset.data[data_id]
          wav_random = data_item["file_name"]
          return wav_random, read_audio(wav_random, hparams)
  
      dataset.add_dynamic_item(extra_random_sample)
  
imports: import logging
  import sys
  import torch
  import speechbrain
  import os
  from collections import namedtuple
  from audiomnist_prepare import prepare_audiomnist
  from hyperpyyaml import load_hyperpyyaml
  from torchaudio import functional
  from speechbrain.dataio.dataio import length_to_mask, write_audio
  from speechbrain.dataio.dataset import apply_overfit_test
  from speechbrain.utils import data_utils
  from speechbrain.utils.train_logger import plot_spectrogram
  from enum import Enum
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.utils.data_utils import dict_value_combinations, dist_stats, masked_mean, masked_min, masked_max, masked_std, match_shape
label_desc: Use image node_id 6 for calling a global function with example usage: enhance_with_random(dataset, hparams) and returns: wav_random,  read_audio