code: def make_classifier_pickle(
      answers, classes, class_names, name, feature_names
  ):
      """Make a pickle of a fake classifier
  
      answers - the answers you want to get back after calling classifier.predict
      classes - the class #s for the answers.
      class_names - one name per class in the order they appear in classes
      name - the name of the classifier
      feature_names - the names of the features fed into the classifier
      """
      proba = numpy.zeros([len(answers), len(classes)])
      classifier = FakeClassifier(answers, classes, proba)
      return pickle.dumps(
          [classifier, class_names, name, feature_names]
      )
  
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 16 for calling a global function with example usage: make_classifier_pickle(answers, classes, class_names, name, feature_names) and returns: pickle