code: def prepare_group(self, workspace, grouping, image_numbers):
      image_set_list = workspace.image_set_list
      pipeline = workspace.pipeline
      assert isinstance(pipeline, Pipeline)
      m = workspace.measurements
      assert isinstance(m, Measurements)
      if self.each_or_all != EA_EACH and len(image_numbers) > 0:
          title = "#%d: CorrectIlluminationCalculate for %s" % (
              self.module_num,
              self.image_name,
          )
          message = (
              "CorrectIlluminationCalculate is averaging %d images while "
              "preparing for run" % (len(image_numbers))
          )
          output_image_provider = CorrectIlluminationImageProvider(
              self.illumination_image_name.value, self
          )
          d = self.get_dictionary(image_set_list)[OUTPUT_IMAGE] = {}
          if self.each_or_all == EA_ALL_FIRST:
              #
              # Find the module that provides the image we need
              #
              md = workspace.pipeline.get_provider_dictionary(
                  self.image_name.group, self
              )
              src_module, src_setting = md[self.image_name.value][-1]
              modules = list(pipeline.modules())
              idx = modules.index(src_module)
              last_module = modules[idx + 1]
              for w in pipeline.run_group_with_yield(
                  workspace,
                  grouping,
                  image_numbers,
                  last_module,
                  title,
                  message,
              ):
                  image = w.image_set.get_image(
                      self.image_name.value, cache=False
                  )
                  output_image_provider.add_image(image)
                  w.image_set.clear_cache()
          output_image_provider.serialize(d)
  
      return True
  
imports: import centrosome.bg_compensate
  import centrosome.cpmorphology
  import centrosome.cpmorphology
  import centrosome.filter
  import centrosome.smooth
  import numpy
  import scipy.ndimage
  import skimage.filters
  from cellprofiler_core.image import AbstractImage
  from cellprofiler_core.image import Image
  from cellprofiler_core.measurement import Measurements
  from cellprofiler_core.module import Module
  from cellprofiler_core.pipeline import Pipeline
  from cellprofiler_core.setting import Binary
  from cellprofiler_core.setting import ValidationError
  from cellprofiler_core.setting.choice import Choice
  from cellprofiler_core.setting.subscriber import ImageSubscriber
  from cellprofiler_core.setting.text import Float
  from cellprofiler_core.setting.text import ImageName
  from cellprofiler_core.setting.text import Integer
label_desc: Use image node_id 5 for calling the CorrectIlluminationCalculate obj's underlying member method code with example usage: obj.prepare_group(workspace, grouping, image_numbers) and returns: True