code: def test_classify_one():
      classes = ["a", "b"]
      labels = numpy.zeros((10, 10), int)
      labels[4:7, 4:7] = 1
      workspace, module = make_workspace_classifier(
          {OBJECTS_NAME: labels}, grouplist=classes
      )
      with make_classifier(module, [1], class_names=classes):
          workspace.measurements[OBJECTS_NAME, TEST_FTR] = numpy.zeros(
              1
          )
          module.run(workspace)
          for eachclass in classes:
              output_objects = workspace.object_set.get_objects(
                  eachclass
              )
              output_object_count = output_objects.count
              if eachclass == "a":
                  assert output_object_count == 1
              else:
                  assert output_object_count == 0
  
imports: import contextlib
  import os
  import pickle
  import tempfile
  import numpy
  import pytest
  import cellprofiler_core.image
  import cellprofiler_core.measurement
  from cellprofiler_core.constants.measurement import COLTYPE_FLOAT, COLTYPE_INTEGER
  import cellprofiler.modules.classifyobjects
  import cellprofiler_core.object
  import cellprofiler_core.pipeline
  import cellprofiler_core.setting
  import cellprofiler_core.workspace
label_desc: Use image node_id 13 for calling a global function with example usage: test_classify_one() without return types