code: def fetch_atlas_allen_2011(
      data_dir=None, url=None, resume=True, verbose=1
  ):
      """Download and return file names for the Allen and MIALAB :term:`ICA` \
      :term:`Probabilistic atlas` (dated 2011).
  
      See :footcite:t:`Allen2011`.
  
      The provided images are in MNI152 space.
  
      Parameters
      ----------
      %(data_dir)s
      %(url)s
      %(resume)s
      %(verbose)s
  
      Returns
      -------
      data : :class:`sklearn.utils.Bunch`
          Dictionary-like object, keys are:
  
          - 'maps': :obj:`str`, path to nifti file containing the
            T-maps of all 75 unthresholded components. The image has
            shape ``(53, 63, 46, 75)``.
          - 'rsn28': :obj:`str`, path to nifti file containing the
            T-maps of 28 RSNs included in :footcite:t:`Allen2011`.
            The image has shape ``(53, 63, 46, 28)``.
          - 'networks': :obj:`list` of :obj:`list` of :obj:`str`, list
            containing the names for the 28 RSNs.
          - 'rsn_indices': :obj:`list` of :obj:`tuple`, each tuple is a
            (:obj:`str`, :obj:`list` of :`int`). This maps the network names
            to the map indices. For example, the map indices for the 'Visual'
            network can be obtained:
  
              .. code-block:: python
  
                  # Should return [46, 64, 67, 48, 39, 59]
                  dict(data.rsn_indices)["Visual"]
  
          - 'comps': :obj:`str`, path to nifti file containing the
            aggregate :term:`ICA` components.
          - 'description': :obj:`str`, description of the dataset.
  
      References
      ----------
      .. footbibliography::
  
      Notes
      -----
      Licence: unknown
  
      See http://mialab.mrn.org/data/index.html for more information
      on this dataset.
  
      """
      if url is None:
          url = "https://osf.io/hrcku/download"
  
      dataset_name = "allen_rsn_2011"
      keys = ("maps", "rsn28", "comps")
  
      opts = {"uncompress": True}
      files = [
          "ALL_HC_unthresholded_tmaps.nii.gz",
          "RSN_HC_unthresholded_tmaps.nii.gz",
          "rest_hcp_agg__component_ica_.nii.gz",
      ]
  
      labels = [
          ("Basal Ganglia", [21]),
          ("Auditory", [17]),
          ("Sensorimotor", [7, 23, 24, 38, 56, 29]),
          ("Visual", [46, 64, 67, 48, 39, 59]),
          ("Default-Mode", [50, 53, 25, 68]),
          ("Attentional", [34, 60, 52, 72, 71, 55]),
          ("Frontal", [42, 20, 47, 49]),
      ]
  
      networks = [[name] * len(idxs) for name, idxs in labels]
  
      filenames = [
          (os.path.join("allen_rsn_2011", f), url, opts) for f in files
      ]
  
      data_dir = get_dataset_dir(
          dataset_name, data_dir=data_dir, verbose=verbose
      )
      sub_files = fetch_files(
          data_dir, filenames, resume=resume, verbose=verbose
      )
  
      fdescr = get_dataset_descr(dataset_name)
  
      params = [
          ("description", fdescr),
          ("rsn_indices", labels),
          ("networks", networks),
          *list(zip(keys, sub_files)),
      ]
      return Bunch(**dict(params))
  
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 18 for calling a global function with example usage: fetch_atlas_allen_2011(data_dir, url, resume, verbose) and returns: Bunch