How to use the nilearn.datasets.utils._fetch_files function in nilearn

split_name = atlas_name.split('cort')
            atlas_name = 'cortl' + split_name[1]
            label_file = 'HarvardOxford-Cortical-Lateralized.xml'
            lateralized = True
        else:
            label_file = 'HarvardOxford-Cortical.xml'
            lateralized = False
    else:
        label_file = 'HarvardOxford-Subcortical.xml'
        lateralized = False
    label_file = os.path.join(root, label_file)

    atlas_file = os.path.join(root, 'HarvardOxford',
                              'HarvardOxford-' + atlas_name + '.nii.gz')

    atlas_img, label_file = _fetch_files(
        data_dir,
        [(atlas_file, url, opts), (label_file, url, opts)],
        resume=resume, verbose=verbose)

    names = {}
    from xml.etree import ElementTree
    names[0] = 'Background'
    for label in ElementTree.parse(label_file).findall('.//label'):
        names[int(label.get('index')) + 1] = label.text
    names = list(names.values())

    if not symmetric_split:
        return Bunch(maps=atlas_img, labels=names)

    if atlas_name in ("cort-prob-1mm", "cort-prob-2mm",
                      "sub-prob-1mm", "sub-prob-2mm"):

"mwc2OAS1_%04d_MR1_mpr_anon_fslswapdim_bet.nii.gz")
             % (s, s),
             url_images, opts)
            for s in range(1, 457) if s not in missing_subjects]
    file_names_extvars = [("oasis_cross-sectional.csv", url_csv, {})]
    file_names_dua = [("data_usage_agreement.txt", url_dua, {})]
    # restrict to user-specified number of subjects
    file_names_gm = file_names_gm[:n_subjects]
    file_names_wm = file_names_wm[:n_subjects]

    file_names = (file_names_gm + file_names_wm +
                  file_names_extvars + file_names_dua)
    dataset_name = 'oasis1'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    files = _fetch_files(data_dir, file_names, resume=resume,
                         verbose=verbose)

    # Build Bunch
    gm_maps = files[:n_subjects]
    wm_maps = files[n_subjects:(2 * n_subjects)]
    ext_vars_file = files[-2]
    data_usage_agreement = files[-1]

    # Keep CSV information only for selected subjects
    csv_data = np.recfromcsv(ext_vars_file)
    # Comparisons to recfromcsv data must be bytes.
    actual_subjects_ids = [("OAS1" +
                            str.split(os.path.basename(x),
                                      "OAS1")[1][:9]).encode()
                           for x in gm_maps]
    subject_mask = np.asarray([subject_id in actual_subjects_ids

-------
    urls_path: string
        Path to downloaded dataset index

    urls: list of string
        Sorted list of dataset directories
    """
    data_prefix = '{}/{}/uncompressed'.format(dataset_version.split('_')[0],
                                              dataset_version,
                                              )
    data_dir = _get_dataset_dir(data_prefix, data_dir=data_dir,
                                verbose=verbose)

    file_url = 'https://osf.io/86xj7/download'
    final_download_path = os.path.join(data_dir, 'urls.json')
    downloaded_file_path = _fetch_files(data_dir=data_dir,
                                        files=[(final_download_path,
                                                file_url,
                                                {'move': final_download_path}
                                                )],
                                        resume=True
                                        )
    urls_path = downloaded_file_path[0]
    with open(urls_path, 'r') as json_file:
        urls = json.load(json_file)
    return urls_path, urls

"list. Subjects must be selected in "
                                 "[1, 2, 3, 4, 5, 6]".format(sub_id))

    dataset_name = 'haxby2001'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)

    # Get the mask
    url_mask = 'https://www.nitrc.org/frs/download.php/7868/mask.nii.gz'
    mask = _fetch_files(data_dir, [('mask.nii.gz', url_mask, {})],
                        verbose=verbose)[0]

    # Dataset files
    if url is None:
        url = 'http://data.pymvpa.org/datasets/haxby2001/'
    md5sums = _fetch_files(data_dir, [('MD5SUMS', url + 'MD5SUMS', {})],
                           verbose=verbose)[0]
    md5sums = _read_md5_sum_file(md5sums)

    # definition of dataset files
    sub_files = ['bold.nii.gz', 'labels.txt',
                 'mask4_vt.nii.gz', 'mask8b_face_vt.nii.gz',
                 'mask8b_house_vt.nii.gz', 'mask8_face_vt.nii.gz',
                 'mask8_house_vt.nii.gz', 'anat.nii.gz']
    n_files = len(sub_files)

    if subjects is None:
        subjects = []

    if isinstance(subjects, numbers.Number):
        subject_mask = np.arange(1, subjects + 1)
    else:

keys = ['components_64',
            'components_128',
            'components_453_gm',
            'loadings_128_gm'
            ]

    paths = [
        'components_64.nii.gz',
        'components_128.nii.gz',
        'components_453_gm.nii.gz',
        'loadings_128_gm.npy',
    ]
    urls = [url + path for path in paths]
    files = [(path, url, {}) for path, url in zip(paths, urls)]

    files = _fetch_files(data_dir, files, resume=resume,
                         verbose=verbose)

    params = {key: file for key, file in zip(keys, files)}

    fdescr = 'Components computed using the MODL package, at various scale,' \
             'from HCP900 data'

    params['description'] = fdescr
    params['data_dir'] = data_dir

    return Bunch(**params)

'RHV3A.nii.gz',
        'RHV3.nii.gz',
        'RHV4v.nii.gz',
        'RHVP.nii.gz'
    ]

    file_mask = [(os.path.join('mask', m), url, opts) for m in file_mask]

    file_names = func_figure + func_random + \
                 label_figure + label_random + \
                 file_mask

    dataset_name = 'miyawaki2008'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    files = _fetch_files(data_dir, file_names, resume=resume, verbose=verbose)

    # Fetch the background image
    bg_img = _fetch_files(data_dir, [('bg.nii.gz', url, opts)], resume=resume,
                          verbose=verbose)[0]

    fdescr = _get_dataset_descr(dataset_name)

    # Return the data
    return Bunch(
        func=files[:32],
        label=files[32:64],
        mask=files[64],
        mask_roi=files[65:],
        background=bg_img,
        description=fdescr)

Notes
	-----
	Please deprecate this function.
	We should distribute dependencies such as atlases in the form of unambiguously managed packages, and not data fetched from the web at runtime.

	References
	----------
	.. [1] 'Papp, Eszter A., et al. "Waxholm Space atlas of the Sprague Dawley rat brain." NeuroImage 97 (2014): 374-386.'
	.. [2] https://www.nitrc.org/projects/whs-sd-atlas

	"""
	from nilearn.datasets.utils import _fetch_files
	# Get template
	url_template = 'https://www.nitrc.org/frs/download.php/9423/WHS_SD_rat_T2star_v1.01.nii.gz'
	template = _fetch_files(path.abspath(path.expanduser(template_dir)), [('WHS_SD_rat_T2star_v1.01.nii.gz', url_template, {})],
			verbose=verbose)[0]

	# Get atlas
	url_atlas = 'https://www.nitrc.org/frs/download.php/9438/WHS_SD_rat_atlas_v2.nii.gz'
	atlas = _fetch_files(path.abspath(path.expanduser(template_dir)), [('WHS_SD_rat_atlas_v2.nii.gz', url_atlas, {})],
			verbose=verbose)[0]

	# Get labels
	url_labels = 'https://www.nitrc.org/frs/download.php/9439/WHS_SD_rat_atlas_v2.label'
	labels = _fetch_files(path.abspath(path.expanduser(template_dir)), [('WHS_SD_rat_atlas_v2.label', url_labels, {})],
			verbose=verbose)[0]

	# resample template
	commands = ["ResampleImage 3 WHS_SD_rat_T2star_v1.01.nii.gz _200micron_WHS_SD_rat_T2star_v1.01.nii.gz 0.2x0.2x0.2 size=1 spacing=0 4",
		"SmoothImage 3 _200micron_WHS_SD_rat_T2star_v1.01.nii.gz 0.4 200micron_WHS_SD_rat_T2star_v1.01.nii.gz",
		"rm _200micron_WHS_SD_rat_T2star_v1.01.nii.gz",

if n_subjects > max_subjects:
        warnings.warn('Warning: there are only %d subjects' % max_subjects)
        n_subjects = max_subjects
    ids = ids[:n_subjects]
    nitrc_ids = nitrc_ids[:n_subjects]

    opts = dict(uncompress=True)

    # Dataset description
    fdescr = _get_dataset_descr(dataset_name)

    # First, get the metadata
    phenotypic = ('ADHD200_40subs_motion_parameters_and_phenotypics.csv',
        url + '7781/adhd40_metadata.tgz', opts)

    phenotypic = _fetch_files(data_dir, [phenotypic], resume=resume,
                              verbose=verbose)[0]

    # Load the csv file
    phenotypic = np.genfromtxt(phenotypic, names=True, delimiter=',',
                               dtype=None)

    # Keep phenotypic information for selected subjects
    int_ids = np.asarray(ids, dtype=int)
    phenotypic = phenotypic[[np.where(phenotypic['Subject'] == i)[0][0]
                             for i in int_ids]]

    # Download dataset files

    archives = [url + '%i/adhd40_%s.tgz' % (ni, ii)
                for ni, ii in zip(nitrc_ids, ids)]
    functionals = ['data/%s/%s_rest_tshift_RPI_voreg_mni.nii.gz' % (i, i)

def fetch_mask(data_dir=None, url=None, resume=True, verbose=1):
    if url is None:
        url = 'http://cogspaces.github.io/assets/data/hcp_mask.nii.gz'
    files = [('hcp_mask.nii.gz', url, {})]

    dataset_name = 'mask'
    data_dir = get_data_dir(data_dir)
    dataset_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                   verbose=verbose)
    files = _fetch_files(dataset_dir, files, resume=resume,
                         verbose=verbose)
    return files[0]

Notes
    -----
    See `more information about datasets structure
    `_
    """

    if url is None:
        # Here we use the file that provides URL for all others
        url = 'https://api.figshare.com/v2/articles/4197885'
    dataset_name = 'cobre'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    fdescr = _get_dataset_descr(dataset_name)

    # First, fetch the file that references all individual URLs
    files = _fetch_files(data_dir, [("4197885", url, {})],
                         verbose=verbose)[0]

    files = json.load(open(files, 'r'))
    files = files['files']
    # Index files by name
    files_ = {}
    for f in files:
        files_[f['name']] = f
    files = files_

    # Fetch the phenotypic file and load it
    csv_name_gz = 'phenotypic_data.tsv.gz'
    csv_name = os.path.splitext(csv_name_gz)[0]
    csv_file_phen = _fetch_files(
        data_dir, [(csv_name, files[csv_name_gz]['download_url'],
                    {'md5': files[csv_name_gz].get('md5', None),