code: def test_line():
      """Test the alternate centers with a line"""
      labels = numpy.array(
          [
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
              [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
              [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
          ]
      )
      centers = numpy.zeros(labels.shape, int)
      centers[2, 1] = 1
      distance_to_center = numpy.array(
          [
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 1, 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 0],
              [0, 0, 1, 2, 3, 4, 5, 6, 7, 0],
              [0, 1, 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
          ]
      )
      distance_to_edge = numpy.array(
          [
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
              [0, 1, 2, 2, 2, 2, 2, 2, 2, 0],
              [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
          ]
      )
      numpy.random.seed(0)
      image = numpy.random.uniform(size=labels.shape)
      m = run_module(image, labels, centers)
      total_intensity = numpy.sum(image[labels == 1])
      normalized_distance = distance_to_center / (
          distance_to_center + distance_to_edge + 0.001
      )
      bin_labels = (normalized_distance * 4).astype(int)
      for bin in range(1, 5):
          data = m.get_current_measurement(
              OBJECT_NAME, feature_frac_at_d(bin, 4)
          )
          assert len(data) == 1
          bin_intensity = numpy.sum(
              image[(labels == 1) & (bin_labels == bin - 1)]
          )
          expected = bin_intensity / total_intensity
          assert numpy.abs(expected - data[0]) < 0.1 * expected
          data = m.get_current_measurement(
              OBJECT_NAME, feature_mean_frac(bin, 4)
          )
          expected = (
              expected
              * numpy.sum(labels == 1)
              / numpy.sum((labels == 1) & (bin_labels == bin - 1))
          )
          assert numpy.abs(data[0] - expected) < 0.1 * expected
          data = m.get_current_measurement(
              OBJECT_NAME, feature_radial_cv(bin, 4)
          )
          assert len(data) == 1
  
imports: import io
  import centrosome.propagate
  import numpy
  import scipy.stats
  import cellprofiler_core.image
  import cellprofiler_core.measurement
  import cellprofiler_core.module
  from cellprofiler_core.constants.measurement import COLTYPE_FLOAT
  import cellprofiler.modules.measureobjectintensitydistribution
  import cellprofiler_core.object
  import cellprofiler_core.pipeline
  import cellprofiler_core.preferences
  import cellprofiler_core.setting
  import cellprofiler_core.workspace
  import tests.frontend.modules
label_desc: Use image node_id 19 for calling a global function with example usage: test_line() without return types