code: def fetch_atlas_msdl(data_dir=None, url=None, resume=True, verbose=1):
      """Download and load the MSDL brain :term:`Probabilistic atlas`.
  
      It can be downloaded at :footcite:t:`atlas_msdl`, and cited
      using :footcite:t:`Varoquaux2011`.
      See also :footcite:t:`Varoquaux2013` for more information.
  
      Parameters
      ----------
      %(data_dir)s
      %(url)s
      %(resume)s
      %(verbose)s
  
      Returns
      -------
      data : :class:`sklearn.utils.Bunch`
          Dictionary-like object, the interest attributes are :
  
          - 'maps': :obj:`str`, path to nifti file containing the
            :term:`Probabilistic atlas` image (shape is equal to
            ``(40, 48, 35, 39)``).
          - 'labels': :obj:`list` of :obj:`str`, list containing the labels
            of the regions. There are 39 labels such that ``data.labels[i]``
            corresponds to map ``i``.
          - 'region_coords': :obj:`list` of length-3 :obj:`tuple`,
            ``data.region_coords[i]`` contains the coordinates ``(x, y, z)``
            of region ``i`` in :term:`MNI` space.
          - 'networks': :obj:`list` of :obj:`str`, list containing the names
            of the networks. There are 39 network names such that
            ``data.networks[i]`` is the network name of region ``i``.
          - 'description': :obj:`str`, description of the atlas.
  
      References
      ----------
      .. footbibliography::
  
  
      """
      url = "https://team.inria.fr/parietal/files/2015/01/MSDL_rois.zip"
      opts = {"uncompress": True}
  
      dataset_name = "msdl_atlas"
      files = [
          (
              os.path.join("MSDL_rois", "msdl_rois_labels.csv"),
              url,
              opts,
          ),
          (os.path.join("MSDL_rois", "msdl_rois.nii"), url, opts),
      ]
  
      data_dir = get_dataset_dir(
          dataset_name, data_dir=data_dir, verbose=verbose
      )
      files = fetch_files(
          data_dir, files, resume=resume, verbose=verbose
      )
      csv_data = pd.read_csv(files[0])
      labels = [name.strip() for name in csv_data["name"].tolist()]
  
      with warnings.catch_warnings():
          warnings.filterwarnings(
              "ignore", module="numpy", category=FutureWarning
          )
          region_coords = csv_data[["x", "y", "z"]].values.tolist()
      net_names = [
          net_name.strip() for net_name in csv_data["net name"].tolist()
      ]
      fdescr = get_dataset_descr(dataset_name)
  
      return Bunch(
          maps=files[1],
          labels=labels,
          region_coords=region_coords,
          networks=net_names,
          description=fdescr,
      )
  
imports: import json
  import os
  import re
  import shutil
  import warnings
  import xml.etree.ElementTree
  from pathlib import Path
  from tempfile import mkdtemp
  import nibabel
  import numpy
  import pandas
  from sklearn.utils import Bunch
  from .._utils import check_niimg, fill_doc
  from ..image import get_data, new_img_like, reorder_img
  from ._utils import fetch_files, get_dataset_descr, get_dataset_dir
label_desc: Use image node_id 10 for calling a global function with example usage: fetch_atlas_msdl(data_dir, url, resume, verbose) and returns: Bunch