code: def test_classify_many():
      classes = ["a", "b"]
      labels = numpy.zeros((10, 10), int)
      labels[1:4, 1:4] = 1
      labels[5:7, 5:7] = 2
      workspace, module = make_workspace_classifier(
          {OBJECTS_NAME: labels}, grouplist=classes
      )
      with make_classifier(
          module, numpy.array([1, 2]), class_names=classes
      ):
          workspace.measurements[OBJECTS_NAME, TEST_FTR] = numpy.zeros(
              2
          )
          module.run(workspace)
          for eachclass in classes:
              output_objects = workspace.object_set.get_objects(
                  eachclass
              )
              output_objects = workspace.object_set.get_objects(
                  eachclass
              )
              assert output_objects.count == 1
              labels_out = output_objects.get_labels()[0][0]
              if eachclass == "a":
                  numpy.testing.assert_array_equal(
                      labels_out[1:4, 1:4], 1
                  )
                  numpy.testing.assert_array_equal(
                      labels_out[5:7, 5:7], 0
                  )
              else:
                  numpy.testing.assert_array_equal(
                      labels_out[1:4, 1:4], 0
                  )
                  numpy.testing.assert_array_equal(
                      labels_out[5:7, 5:7], 1
                  )
  
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 14 for calling a global function with example usage: test_classify_many() without return types