code: def test_rename_output_nn_classifier(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)
  
      class_labels = ["a", "b", "c"]
      mlmodel = make_nn_classifier(
          mlmodel, class_labels=["a", "b", "c"]
      )
  
      # rename output
      spec = mlmodel.get_spec()
      rename_feature(spec, "out", "new_out_name")
      mlmodel = MLModel(spec)
  
      out_dict = mlmodel.predict(
          {"data": np.array([4.0, 5.5, 6.0])}, useCPUOnly=True
      )
      self.assertEqual(out_dict["classLabel"], "c")
      self.assertTrue("new_out_name" in out_dict)
      self.assertTrue(isinstance(out_dict["new_out_name"], dict))
  
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 19 for calling the MLModelTest obj's underlying member method code with example usage: obj.test_rename_output_nn_classifier() without return types