code: def display_two_measurements(self, workspace, figure):
      figure.set_subplots((2, 2))
      object_name = self.object_name.value
      for i, feature_name in (
          (0, self.first_measurement.value),
          (1, self.second_measurement.value),
      ):
          saved_values = workspace.display_data.saved_values[i]
          good_saved_values = saved_values[~numpy.isnan(saved_values)]
          if len(good_saved_values) == 0:
              figure.subplot_table(
                  i, 0, [["No %s objects found" % object_name]]
              )
          else:
              axes = figure.subplot(i, 0)
              axes.hist(good_saved_values)
              axes.set_xlabel(feature_name)
              axes.set_ylabel("# of %s" % object_name)
      class_1, class_2 = workspace.display_data.in_high_class
      object_codes = class_1.astype(int) + class_2.astype(int) * 2 + 1
      object_codes = numpy.hstack(([0], object_codes))
      nobjects = len(class_1)
      mapping = numpy.zeros(nobjects + 1, int)
      mapping[1:] = numpy.arange(1, nobjects + 1)
      for i in range(2):
          saved_values = workspace.display_data.saved_values[i]
          mapping[1:][numpy.isnan(saved_values)] = 0
      labels = object_codes[mapping[workspace.display_data.labels]]
      figure.subplot_imshow_labels(0, 1, labels, title=object_name)
      #
      # Draw a 4-bar histogram
      #
      axes = figure.subplot(1, 1)
      values = object_codes[1:]
      axes.hist(values[~numpy.isnan(values)], bins=4, range=(0.5, 4.5))
      axes.set_xticks((1, 2, 3, 4))
      if self.wants_custom_names:
          axes.set_xticklabels(
              (
                  self.low_low_custom_name.value,
                  self.high_low_custom_name.value,
                  self.low_high_custom_name.value,
                  self.high_high_custom_name.value,
              )
          )
      else:
          axes.set_xticklabels(
              ("low\nlow", "high\nlow", "low\nhigh", "high\nhigh")
          )
      axes.set_ylabel("# of %s" % object_name)
      colors = self.get_colors(len(axes.patches))
      #
      # The patches are the rectangles in the histogram
      #
      for i, patch in enumerate(axes.patches):
          patch.set_facecolor(colors[i + 1, :])
  
imports: import functools
  import os
  import numpy
  from cellprofiler.modules import _help
  from cellprofiler.utilities.rules import Rules
  from cellprofiler_core.constants.measurement import COLTYPE_FLOAT, M_LOCATION_CENTER_X, M_LOCATION_CENTER_Y, COLTYPE_VARCHAR, C_COUNT, C_CHILDREN, C_LOCATION, C_NUMBER, C_PARENT, FTR_OBJECT_NUMBER, FTR_CENTER_X, FTR_CENTER_Y, FTR_CENTER_Z
  from cellprofiler_core.constants.measurement import COLTYPE_INTEGER
  from cellprofiler_core.constants.measurement import IMAGE
  from cellprofiler_core.image import Image
  from cellprofiler_core.module import Module
  from cellprofiler_core.object import Objects
  from cellprofiler_core.preferences import get_default_colormap, get_headless
  from cellprofiler_core.setting import Binary
  from cellprofiler_core.setting import Divider
  from cellprofiler_core.setting import HiddenCount
  from cellprofiler_core.setting import Measurement
  from cellprofiler_core.setting import SettingsGroup
  from cellprofiler_core.setting import ValidationError
  from cellprofiler_core.setting.choice import Choice
  from cellprofiler_core.setting.do_something import DoSomething
  from cellprofiler_core.setting.do_something import RemoveSettingButton
  from cellprofiler_core.setting.subscriber import LabelSubscriber
  from cellprofiler_core.setting.text import Alphanumeric, Directory, Filename, LabelName
  from cellprofiler_core.setting.text import Float
  from cellprofiler_core.setting.text import ImageName
  from cellprofiler_core.setting.text import Integer
  from cellprofiler_core.setting.text import Text
  from cellprofiler_core.constants.measurement import FF_CHILDREN_COUNT
  from cellprofiler_core.constants.measurement import FF_COUNT
  from cellprofiler_core.constants.measurement import FF_PARENT
  from cellprofiler_core.module.image_segmentation import ImageSegmentation
label_desc: Use image node_id 12 for calling the ClassifyObjects obj's underlying member method code with example usage: obj.display_two_measurements(workspace, figure) without return types