code: def fetch_coords_dosenbach_2010(
      ordered_regions=True, legacy_format=True
  ):
      """Load the Dosenbach et al 160 ROIs.
  
      These ROIs cover much of the cerebral cortex
      and cerebellum and are assigned to 6 networks.
  
      See :footcite:t:`Dosenbach2010`.
  
      Parameters
      ----------
      ordered_regions : :obj:`bool`, default=True
          ROIs from same networks are grouped together and ordered with respect
          to their names and their locations (anterior to posterior).
      %(legacy_format)s
  
      Returns
      -------
      data : :class:`sklearn.utils.Bunch`
          Dictionary-like object, contains:
  
          - 'rois': :class:`numpy.recarray`, rec array with the coordinates
            of the 160 ROIs in :term:`MNI` space.
            If ``legacy_format`` is set to ``False``, this is a
            :class:`pandas.DataFrame`.
          - 'labels': :class:`numpy.ndarray` of :obj:`str`, list of label
            names for the 160 ROIs.
          - 'networks': :class:`numpy.ndarray` of :obj:`str`, list of network
            names for the 160 ROI.
          - 'description': :obj:`str`, description of the dataset.
  
      References
      ----------
      .. footbibliography::
  
      """
      dataset_name = "dosenbach_2010"
      fdescr = get_dataset_descr(dataset_name)
      package_directory = os.path.dirname(os.path.abspath(__file__))
      csv = os.path.join(
          package_directory, "data", "dosenbach_2010.csv"
      )
      out_csv = pd.read_csv(csv)
  
      if ordered_regions:
          out_csv = out_csv.sort_values(by=["network", "name", "y"])
  
      # We add the ROI number to its name, since names are not unique
      names = out_csv["name"]
      numbers = out_csv["number"]
      labels = np.array(
          [f"{name} {number}" for (name, number) in zip(names, numbers)]
      )
      params = dict(
          rois=out_csv[["x", "y", "z"]],
          labels=labels,
          networks=out_csv["network"],
          description=fdescr,
      )
  
      if legacy_format:
          warnings.warn(_LEGACY_FORMAT_MSG, DeprecationWarning)
          params["rois"] = params["rois"].to_records(index=False)
  
      return Bunch(**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 16 for calling a global function with example usage: fetch_coords_dosenbach_2010(ordered_regions, legacy_format) and returns: Bunch