code: def display(self, workspace, figure):
      # these are actually just the pixel data
      avg_image = workspace.display_data.avg_image
      dilated_image = workspace.display_data.dilated_image
      output_image = workspace.display_data.output_image
  
      figure.set_subplots((2, 2))
  
      def imshow(x, y, image, *args, **kwargs):
          if image.ndim == 2:
              f = figure.subplot_imshow_grayscale
          else:
              f = figure.subplot_imshow_color
          return f(x, y, image, *args, **kwargs)
  
      imshow(0, 0, avg_image, "Averaged image")
      pixel_data = output_image
      imshow(
          0,
          1,
          output_image,
          "Final illumination function",
          sharexy=figure.subplot(0, 0),
      )
      imshow(
          1,
          0,
          dilated_image,
          "Dilated image",
          sharexy=figure.subplot(0, 0),
      )
      statistics = [
          ["Min value", round(numpy.min(output_image), 2)],
          ["Max value", round(numpy.max(output_image), 2)],
          ["Calculation type", self.intensity_choice.value],
      ]
      if self.intensity_choice == IC_REGULAR:
          statistics.append(
              ["Radius", self.object_dilation_radius.value]
          )
      elif self.smoothing_method != SM_SPLINES:
          statistics.append(["Block size", self.block_size.value])
      statistics.append(["Rescaling?", self.rescale_option.value])
      statistics.append(["Each or all?", self.each_or_all.value])
      statistics.append(
          ["Smoothing method", self.smoothing_method.value]
      )
      statistics.append(
          [
              "Smoothing filter size",
              round(self.smoothing_filter_size(output_image.size), 2),
          ]
      )
      figure.subplot_table(
          1,
          1,
          [[x[1]] for x in statistics],
          row_labels=[x[0] for x in statistics],
      )
  
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 9 for calling the CorrectIlluminationCalculate obj's underlying member method code with example usage: obj.display(workspace, figure) and returns: f