code: def fetch_atlas_destrieux_2009(
      lateralized=True,
      data_dir=None,
      url=None,
      resume=True,
      verbose=1,
      legacy_format=True,
  ):
      """Download and load the Destrieux cortical \
      :term:`deterministic atlas<Deterministic atlas>` (dated 2009).
  
      See :footcite:t:`Fischl2004`,
      and :footcite:t:`Destrieux2009`.
  
      .. note::
  
          Some labels from the list of labels might not be present in the
          atlas image, in which case the integer values in the image might
          not be consecutive.
  
      Parameters
      ----------
      lateralized : :obj:`bool`, default=True
          If True, returns an atlas with distinct regions for right and left
          hemispheres.
      %(data_dir)s
      %(url)s
      %(resume)s
      %(verbose)s
      %(legacy_format)s
  
      Returns
      -------
      data : :class:`sklearn.utils.Bunch`
          Dictionary-like object, contains:
  
              - 'maps': :obj:`str`, path to nifti file containing the
                :class:`~nibabel.nifti1.Nifti1Image` defining the cortical
                ROIs, lateralized or not. The image has shape ``(76, 93, 76)``,
                and contains integer values which can be interpreted as the
                indices in the list of labels.
              - 'labels': :class:`numpy.recarray`, rec array containing the
                names of the ROIs.
                If ``legacy_format`` is set to ``False``, this is a
                :class:`pandas.DataFrame`.
              - 'description': :obj:`str`, description of the atlas.
  
      References
      ----------
      .. footbibliography::
  
      """
      if url is None:
          url = "https://www.nitrc.org/frs/download.php/11942/"
  
      url += "destrieux2009.tgz"
      opts = {"uncompress": True}
      lat = "_lateralized" if lateralized else ""
  
      files = [
          (f"destrieux2009_rois_labels{lat}.csv", url, opts),
          (f"destrieux2009_rois{lat}.nii.gz", url, opts),
          ("destrieux2009.rst", url, opts),
      ]
  
      dataset_name = "destrieux_2009"
      data_dir = get_dataset_dir(
          dataset_name, data_dir=data_dir, verbose=verbose
      )
      files_ = fetch_files(
          data_dir, files, resume=resume, verbose=verbose
      )
  
      params = dict(
          maps=files_[1], labels=pd.read_csv(files_[0], index_col=0)
      )
  
      if legacy_format:
          warnings.warn(_LEGACY_FORMAT_MSG, DeprecationWarning)
          params["labels"] = params["labels"].to_records()
  
      params["description"] = Path(files_[2]).read_text()
      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 3 for calling a global function with example usage: fetch_atlas_destrieux_2009(lateralized, data_dir, url, resume, verbose, legacy_format) and returns: Bunch