code: def test_nn_classifier_util(self):
      input_features = [("data", datatypes.Array(3))]
      output_features = [("out", datatypes.Array(3))]
      builder = NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
      builder.add_activation("linear", "LINEAR", "data", "out")
      spec = builder.spec
      mlmodel = MLModel(spec)
      mlmodel = make_nn_classifier(
          mlmodel,
          class_labels=["a", "b", "c"],
          predicted_feature_name="out_confidence",
          predicted_probabilities_output="out",
      )
      out_dict = mlmodel.predict(
          {"data": np.array([4.0, 5.5, 6.0])}, useCPUOnly=True
      )
      self.assertEqual(out_dict["out_confidence"], "c")
      self.assertEqual(
          mlmodel.get_spec().WhichOneof("Type"),
          "neuralNetworkClassifier",
      )
  
imports: import coremltools
  import pytest
  import unittest
  import tempfile
  import numpy
  from coremltools.proto import Model_pb2
  import PIL.Image
  from coremltools.models.utils import rename_feature, save_spec, _macos_version, _convert_neural_network_spec_weights_to_fp16, _is_macos, convert_double_to_float_multiarray_type
  from coremltools.models import MLModel, datatypes
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models.neural_network.utils import make_image_input, make_nn_classifier
label_desc: Use image node_id 17 for calling the MLModelTest obj's underlying member method code with example usage: obj.test_nn_classifier_util() without return types