code: def test_keep_minimal_most_overlap():
      labels = numpy.zeros((10, 20), int)
      labels[:, :10] = 1
      labels[:, 10:] = 2
      sub_labels = numpy.zeros((10, 20), int)
      sub_labels[2, 4] = 1
      sub_labels[4:6, 8:15] = 2
      sub_labels[8, 15] = 3
      expected = sub_labels * (sub_labels != 3)
      workspace, module = make_workspace(
          {INPUT_OBJECTS: sub_labels, ENCLOSING_OBJECTS: labels}
      )
      module.x_name.value = INPUT_OBJECTS
      module.y_name.value = OUTPUT_OBJECTS
      module.enclosing_object_name.value = ENCLOSING_OBJECTS
      module.measurements[0].measurement.value = TEST_FTR
      module.filter_choice.value = (
          cellprofiler.modules.filterobjects.FI_MINIMAL_PER_OBJECT
      )
      module.per_object_assignment.value = (
          cellprofiler.modules.filterobjects.PO_PARENT_WITH_MOST_OVERLAP
      )
      m = workspace.measurements
      m.add_measurement(INPUT_OBJECTS, TEST_FTR, numpy.array([4, 2, 3]))
      module.run(workspace)
      labels = workspace.object_set.get_objects(OUTPUT_OBJECTS)
      assert numpy.all(labels.segmented == expected)
  
imports: import contextlib
  import io
  import os
  import pickle
  import tempfile
  import numpy
  import pytest
  import cellprofiler_core.image
  import cellprofiler_core.measurement
  import cellprofiler_core.modules
  from cellprofiler_core.constants.measurement import FF_PARENT, FF_COUNT, FF_CHILDREN_COUNT, M_LOCATION_CENTER_X, M_LOCATION_CENTER_Y, M_LOCATION_CENTER_Z, M_NUMBER_OBJECT_NUMBER
  import cellprofiler.modules.filterobjects
  import cellprofiler_core.object
  import cellprofiler_core.pipeline
  import cellprofiler_core.preferences
  import cellprofiler_core.workspace
  import tests.frontend.modules
label_desc: Use image node_id 11 for calling a global function with example usage: test_keep_minimal_most_overlap() without return types