code: def test_volume_masked(
      measurements, module, objects, volume, workspace
  ):
      mask = numpy.ones_like(volume.pixel_data)
  
      mask[:, :, 11:] = 0
  
      volume.mask = mask > 0
  
      labels = skimage.measure.label(volume.pixel_data > 0)
  
      objects.segmented = labels
  
      module.run(workspace)
  
      expected = {
          "Intensity_IntegratedIntensity_MyImage": [194.0, 0.0],
          "Intensity_MeanIntensity_MyImage": [
              0.37669902912621361,
              numpy.nan,
          ],
          "Intensity_StdIntensity_MyImage": [
              0.12786816898600722,
              numpy.nan,
          ],
          "Intensity_MinIntensity_MyImage": [0.25, 0.0],
          "Intensity_MaxIntensity_MyImage": [1.0, 0.0],
          "Intensity_IntegratedIntensityEdge_MyImage": [111.0, 0.0],
          "Intensity_MeanIntensityEdge_MyImage": [0.5, numpy.nan],
          "Intensity_StdIntensityEdge_MyImage": [0.0, numpy.nan],
          "Intensity_MinIntensityEdge_MyImage": [0.5, 0.0],
          "Intensity_MaxIntensityEdge_MyImage": [0.5, 0.0],
          "Intensity_MassDisplacement_MyImage": [0.0, numpy.nan],
          "Intensity_LowerQuartileIntensity_MyImage": [0.25, 0.0],
          "Intensity_MedianIntensity_MyImage": [0.5, 0.0],
          "Intensity_MADIntensity_MyImage": [0.0, 0.0],
          "Intensity_UpperQuartileIntensity_MyImage": [0.5, 0.0],
          "Location_CenterMassIntensity_X_MyImage": [5.0, numpy.nan],
          "Location_CenterMassIntensity_Y_MyImage": [5.0, numpy.nan],
          "Location_CenterMassIntensity_Z_MyImage": [5.0, numpy.nan],
          "Location_MaxIntensity_X_MyImage": [5.0, 5.0],
          "Location_MaxIntensity_Y_MyImage": [5.0, 5.0],
          "Location_MaxIntensity_Z_MyImage": [5.0, 0.0],
      }
  
      for feature, value in list(expected.items()):
          actual = measurements.get_measurement(OBJECT_NAME, feature)
  
          numpy.testing.assert_array_almost_equal(
              value,
              actual,
              err_msg="{} expected {}, got {}".format(
                  feature, value, actual
              ),
          )
  
imports: import math
  import centrosome.outline
  import numpy
  import numpy.testing
  import pytest
  import skimage.measure
  import skimage.segmentation
  import cellprofiler_core.image
  import cellprofiler_core.measurement
  from cellprofiler_core.constants.measurement import EXPERIMENT, COLTYPE_FLOAT, C_LOCATION
  import cellprofiler.modules.measureobjectintensity
  import cellprofiler_core.object
  import cellprofiler_core.pipeline
  import cellprofiler_core.preferences
  import cellprofiler_core.workspace
label_desc: Use image node_id 29 for calling a global function with example usage: test_volume_masked(measurements, module, objects, volume, workspace) without return types