code: def test_find_adjacent_by_distance_easy():
      #
      # Feed "find_adjacent_by_distance" points whose "i" are all
      # within the space_distance
      #
      workspace, module = make_workspace(numpy.zeros((20, 10), bool))
      assert isinstance(
          module,
          cellprofiler.modules.identifydeadworms.IdentifyDeadWorms,
      )
      module.space_distance.value = 10
      # Take find_adjacent_by_distance internals into account: consecutive i
      # will create a single cross-product
      #
      i = numpy.arange(10)
      j = numpy.arange(10)
      # Break into two groups: 0-4 (5x5) and 5-9 (5x5)
      j[5:] += 10
      a = numpy.zeros(10)
      first, second = module.find_adjacent_by_distance(i, j, a)
      order = numpy.lexsort((second, first))
      first = first[order]
      second = second[order]
      assert len(first) == 50
      assert len(second) == 50
      for i in range(50):
          assert first[i] == int(i / 5)
      for i in range(25):
          assert second[i] == i % 5
          assert second[i + 25] == (i % 5) + 5
  
imports: import centrosome.cpmorphology
  import numpy
  import scipy.ndimage
  import six.moves
  import cellprofiler_core.image
  import cellprofiler_core.measurement
  import cellprofiler_core.measurement
  from cellprofiler_core.constants.measurement import C_COUNT, M_LOCATION_CENTER_X, M_LOCATION_CENTER_Y, COLTYPE_INTEGER, COLTYPE_FLOAT, M_NUMBER_OBJECT_NUMBER, FF_COUNT
  import cellprofiler.modules.identifydeadworms
  import cellprofiler_core.object
  import cellprofiler_core.pipeline
  import cellprofiler_core.workspace
label_desc: Use image node_id 10 for calling a global function with example usage: test_find_adjacent_by_distance_easy() without return types