code: def read_audio(wav, hparams):
      """Reads an audio file, applying random amplitude
  
      Arguments
      ---------
      wav: str
          the file name (absolute or relative)
      hparams: dict
          hyperparameters
      """
      sig = sb.dataio.dataio.read_audio(wav)
  
      # To Support random amplitude
      if hparams["rand_amplitude"]:
          rand_amp = (
              hparams["max_amp"] - hparams["min_amp"]
          ) * torch.rand(1) + hparams["min_amp"]
          sig = sig / sig.abs().max()
          sig = sig * rand_amp
      return sig
  
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 5 for calling a global function with example usage: read_audio(wav, hparams) and returns: sig