code: def fetch_coords_seitzman_2018(
      ordered_regions=True, legacy_format=True
  ):
      """Load the Seitzman et al. 300 ROIs.
  
      These ROIs cover cortical, subcortical and cerebellar regions and are
      assigned to one of 13 networks (Auditory, CinguloOpercular, DefaultMode,
      DorsalAttention, FrontoParietal, MedialTemporalLobe, ParietoMedial,
      Reward, Salience, SomatomotorDorsal, SomatomotorLateral, VentralAttention,
      Visual) and have a regional label (cortexL, cortexR, cerebellum, thalamus,
      hippocampus, basalGanglia, amygdala, cortexMid).
  
      See :footcite:t:`Seitzman2020`.
  
      .. versionadded:: 0.5.1
  
      Parameters
      ----------
      ordered_regions : :obj:`bool`, default=True
          ROIs from same networks are grouped together and ordered with respect
          to 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 300 ROIs in :term:`MNI` space.
            If ``legacy_format`` is set to ``False``, this is a
            :class:`pandas.DataFrame`.
          - 'radius': :class:`numpy.ndarray` of :obj:`int`, radius of each
            ROI in mm.
          - 'networks': :class:`numpy.ndarray` of :obj:`str`, names of the
            corresponding network for each ROI.
          - 'regions': :class:`numpy.ndarray` of :obj:`str`, names of the
            regions.
          - 'description': :obj:`str`, description of the dataset.
  
      References
      ----------
      .. footbibliography::
  
      """
      dataset_name = "seitzman_2018"
      fdescr = get_dataset_descr(dataset_name)
      package_directory = os.path.dirname(os.path.abspath(__file__))
      roi_file = os.path.join(
          package_directory,
          "data",
          "seitzman_2018_ROIs_300inVol_MNI_allInfo.txt",
      )
      anatomical_file = os.path.join(
          package_directory,
          "data",
          "seitzman_2018_ROIs_anatomicalLabels.txt",
      )
  
      rois = pd.read_csv(roi_file, delimiter=" ")
      rois = rois.rename(
          columns={"netName": "network", "radius(mm)": "radius"}
      )
  
      # get integer regional labels and convert to text labels with mapping
      # from header line
      with open(anatomical_file) as fi:
          header = fi.readline()
      region_mapping = {}
      for r in header.strip().split(","):
          i, region = r.split("=")
          region_mapping[int(i)] = region
  
      anatomical = np.genfromtxt(
          anatomical_file, skip_header=1, encoding=None
      )
      anatomical_names = np.array(
          [region_mapping[a] for a in anatomical]
      )
  
      rois = pd.concat([rois, pd.DataFrame(anatomical_names)], axis=1)
      rois.columns = list(rois.columns[:-1]) + ["region"]
  
      if ordered_regions:
          rois = rois.sort_values(by=["network", "y"])
  
      if legacy_format:
          warnings.warn(_LEGACY_FORMAT_MSG, DeprecationWarning)
          rois = rois.to_records()
  
      params = dict(
          rois=rois[["x", "y", "z"]],
          radius=np.array(rois["radius"]),
          networks=np.array(rois["network"]),
          regions=np.array(rois["region"]),
          description=fdescr,
      )
  
      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 17 for calling a global function with example usage: fetch_coords_seitzman_2018(ordered_regions, legacy_format) and returns: Bunch