code: def test_rename_feature_classifier_mlprogram(self):
      torch_model = _torch.nn.ReLU().eval()
      model = coremltools.convert(
          _torch.jit.trace(
              torch_model,
              _torch.rand(
                  3,
              ),
          ),
          inputs=[coremltools.TensorType(shape=(3,))],
          classifier_config=coremltools.ClassifierConfig(
              ["a", "b", "c"]
          ),
          convert_to="mlprogram",
      )
      spec = model.get_spec()
      input_name = spec.description.input[0].name
  
      rename_feature(spec, "classLabel", "highestProbClass")
      model = coremltools.models.MLModel(
          spec, weights_dir=model.weights_dir
      )
      output_class = model.predict(
          {input_name: np.array([1.0, 2.0, 3.0])}
      )["highestProbClass"]
      self.assertEqual(output_class, "c")
  
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 23 for calling the MLModelTest obj's underlying member method code with example usage: obj.test_rename_feature_classifier_mlprogram() without return types