code: def fetch_sdss_galaxy_images(
      data_home=None, download_if_missing=True
  ):
      """
      Loader for SDSS galaxy images.
  
      A sample of 1000 coloured galaxy image stamps are loaded along with
      labels for their morphological classification.
  
      Parameters
      ----------
      data_home : optional, default=None
          Specify another download and cache folder for the datasets. By default
          all astroML data is stored in '~/astroML_data'.
  
      download_if_missing : optional, default=True
          If False, raise a IOError if the data is not locally available
          instead of trying to download the data from the source site.
  
      Returns
      -------
      data : ndarray, shape = (1000, 68, 68, 3)
          Array containing image data for 1000 galaxies in 3 colours.
  
      labels: ndarray, shape = (1000,)
          Labels of morphological classification (1 for spiral, 0 for elliptical).
  
      Notes
      -----
      The sample selection is courtesy of Marc Huertas-Company from the
      full dataset of Nair & Abraham 2010 ApJS 186:427.
      """
  
      data_home = get_data_home(data_home)
  
      images_file = os.path.join(
          data_home, os.path.basename(IMAGES_URL).split(".gz")[0]
      )
      labels_file = os.path.join(
          data_home, os.path.basename(LABELS_URL)
      )
  
      if not os.path.exists(images_file) or not os.path.exists(
          labels_file
      ):
          if not download_if_missing:
              raise IOError(
                  "data not present on disk. "
                  "set download_if_missing=True to download"
              )
  
          zipped_buf = download_with_progress_bar(
              IMAGES_URL, return_buffer=True
          )
          gzf = GzipFile(fileobj=zipped_buf, mode="rb")
          data = np.load(BytesIO(gzf.read()))
          np.save(images_file, data)
          labels_buffer = download_with_progress_bar(
              LABELS_URL, return_buffer=True
          )
          labels = np.load(labels_buffer)
          np.save(labels_file, labels)
      else:
          data = np.load(images_file)
          labels = np.load(labels_file)
  
      return data, labels
  
imports: import os
  from io import BytesIO
  from gzip import GzipFile
  import numpy
  from astroML.datasets import get_data_home
  from astroML.datasets.tools import download_with_progress_bar
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_sdss_galaxy_images(data_home, download_if_missing) and returns: data,  labels