code: def fetch_spm_auditory(
      data_dir=None,
      data_name="spm_auditory",
      subject_id="sub001",
      verbose=1,
  ):
      """Fetch :term:`SPM` auditory single-subject data.
  
      See :footcite:t:`spm_auditory`.
  
      Parameters
      ----------
      %(data_dir)s
      data_name : :obj:`str`, default='spm_auditory'
          Name of the dataset.
  
      subject_id : :obj:`str`, default='sub001'
          Indicates which subject to retrieve.
      %(verbose)s
  
      Returns
      -------
      data : sklearn.datasets.base.Bunch
          Dictionary-like object, the interest attributes are:
          - 'func': :obj:`list` of :obj:`str`. Paths to functional images
          - 'anat': :obj:`list` of :obj:`str`. Path to anat image
          - 'description': :obj:`str`. Data description
  
      References
      ----------
      .. footbibliography::
  
      """
      data_dir = get_dataset_dir(
          data_name, data_dir=data_dir, verbose=verbose
      )
      subject_dir = os.path.join(data_dir, subject_id)
      if not os.path.exists(subject_dir):
          _download_spm_auditory_data(data_dir, subject_dir, subject_id)
      spm_auditory_data = _prepare_downloaded_spm_auditory_data(
          subject_dir
      )
      try:
          spm_auditory_data["events"]
      except KeyError:
          events_filepath = _make_path_events_file_spm_auditory_data(
              spm_auditory_data
          )
          if not os.path.isfile(events_filepath):
              _make_events_file_spm_auditory_data(events_filepath)
          spm_auditory_data["events"] = events_filepath
      description = get_dataset_descr("spm_auditory")
      spm_auditory_data["description"] = description
      return spm_auditory_data
  
imports: import fnmatch
  import glob
  import json
  import numbers
  import os
  import re
  import warnings
  from io import BytesIO
  from pathlib import Path
  import nibabel
  import nibabel
  import numpy
  import pandas
  from scipy.io import loadmat
  from sklearn.utils import Bunch
  from nilearn.image import get_data
  from .._utils import check_niimg, fill_doc
  from .._utils.numpy_conversions import csv_to_array
  from ._utils import fetch_files, fetch_single_file, filter_columns, get_dataset_descr, get_dataset_dir, read_md5_sum_file, tree, uncompress_file
label_desc: Use image node_id 34 for calling a global function with example usage: fetch_spm_auditory(data_dir, data_name, subject_id, verbose) and returns: spm_auditory_data