code: def display(self, workspace, figure):
      if self.show_window:
          """Display the image and labeling"""
          figure.set_subplots((2, 2))
  
          orig_axes = figure.subplot(0, 0)
          label_axes = figure.subplot(1, 0, sharexy=orig_axes)
          outlined_axes = figure.subplot(0, 1, sharexy=orig_axes)
  
          title = "Input image, cycle #%d" % (
              workspace.measurements.image_number,
          )
          image = workspace.display_data.image
          labeled_image = workspace.display_data.labeled_image
          size_excluded_labeled_image = (
              workspace.display_data.size_excluded_labels
          )
          border_excluded_labeled_image = (
              workspace.display_data.border_excluded_labels
          )
  
          ax = figure.subplot_imshow_grayscale(0, 0, image, title)
          figure.subplot_imshow_labels(
              1, 0, labeled_image, self.y_name.value, sharexy=ax
          )
  
          cplabels = [
              dict(name=self.y_name.value, labels=[labeled_image]),
              dict(
                  name="Objects filtered out by size",
                  labels=[size_excluded_labeled_image],
              ),
              dict(
                  name="Objects touching border",
                  labels=[border_excluded_labeled_image],
              ),
          ]
          if (
              self.unclump_method != UN_NONE
              and self.watershed_method != WA_NONE
              and self.want_plot_maxima
          ):
              # Generate static colormap for alpha overlay
              from matplotlib.colors import ListedColormap
  
              cmap = ListedColormap(self.maxima_color.value)
              if self.maxima_size.value > 1:
                  strel = skimage.morphology.disk(
                      self.maxima_size.value - 1
                  )
                  labels = skimage.morphology.dilation(
                      self.labeled_maxima, footprint=strel
                  )
              else:
                  labels = self.labeled_maxima
              cplabels.append(
                  dict(
                      name="Detected maxima",
                      labels=[labels],
                      mode="alpha",
                      alpha_value=1,
                      alpha_colormap=cmap,
                  )
              )
          title = "%s outlines" % self.y_name.value
          figure.subplot_imshow_grayscale(
              0, 1, image, title, cplabels=cplabels, sharexy=ax
          )
  
          figure.subplot_table(
              1,
              1,
              [[x[1]] for x in workspace.display_data.statistics],
              row_labels=[
                  x[0] for x in workspace.display_data.statistics
              ],
          )
  
imports: import math
  import cellprofiler_core.module.image_segmentation
  import cellprofiler_core.object
  import centrosome.cpmorphology
  import centrosome.outline
  import centrosome.propagate
  import centrosome.threshold
  import numpy
  import scipy.ndimage
  import scipy.sparse
  import skimage.morphology
  import skimage.segmentation
  from cellprofiler_core.setting import Binary, Color
  from cellprofiler_core.setting.choice import Choice
  from cellprofiler_core.setting.range import IntegerRange
  from cellprofiler_core.setting.text import Integer, Float
  import cellprofiler.gui.help
  import cellprofiler.gui.help.content
  from cellprofiler.modules import _help, threshold
label_desc: Use image node_id 16 for calling the IdentifyPrimaryObjects obj's underlying member method code with example usage: obj.display(workspace, figure) without return types