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, compute_units=ComputeUnit.CPU_ONLY)
      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])})
      self.assertEqual(out_dict["out_confidence"], "c")
      self.assertEqual(
          mlmodel.get_spec().WhichOneof("Type"),
          "neuralNetworkClassifier",
      )
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import PIL.Image
  import coremltools
  from coremltools import ComputeUnit
  from coremltools._deps import _HAS_TORCH
  from coremltools.converters.mil import Builder
  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
  from coremltools.models.utils import _convert_neural_network_spec_weights_to_fp16, _is_macos, _macos_version, convert_double_to_float_multiarray_type, rename_feature, save_spec
  from coremltools.proto import Model_pb2
label_desc: Use image node_id 18 for calling the MLModelTest obj's underlying member method code with example usage: obj.test_nn_classifier_util() without return types