code: def fetch_limb(self, diam_sun, base_shape=(1280, 1280)):
          
          from scipy.ndimage.interpolation import zoom
  
          fn = pkg_resources.resource_filename("suvitrainer", "path_length_280_noisy.fits")
          calculated_diam = int(os.path.basename(fn).split("_")[2]) * 2
          with fits.open(fn) as hdus:
              limb_unscaled = hdus[0].data
          scale_factor = diam_sun / calculated_diam
          limb_scaled = zoom(limb_unscaled, scale_factor)
          shape = limb_scaled.shape[0]
          excess = int((shape - base_shape[0]) / 2.0)
          limb_scaled = limb_scaled[excess:shape - excess, excess:shape - excess]
          limb_scaled = limb_scaled[0:base_shape[0], 0:base_shape[1]]
          return "limb", None, limb_scaled
docs: reads the solar limb template file and adjust to the requested solar diameter
          :param diam_sun: diameter of sun in suvi pixels
          :param base_shape: the shape of a suvi image
          :return: product name, None, scaled image