code: def test_rename_image_input(self):
      input_features = [("data", datatypes.Array(3, 1, 1))]
      output_features = [("out", datatypes.Array(3, 1, 1))]
      builder = NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
      builder.add_activation("linear", "LINEAR", "data", "out")
      spec = builder.spec
      # make an image input
      mlmodel = make_image_input(
          MLModel(spec), "data", image_format="NCHW", scale=2.0
      )
      # rename the input
      spec = mlmodel.get_spec()
      rename_feature(spec, "data", "new_input_name")
      mlmodel = MLModel(spec, compute_units=ComputeUnit.CPU_ONLY)
  
      # test
      x = np.array([4, 5, 6], dtype=np.uint8).reshape(1, 1, 3)
      pil_img = PIL.Image.fromarray(x)
      out = mlmodel.predict({"new_input_name": pil_img})["out"]
      np.testing.assert_equal(
          out, np.array([8.0, 10.0, 12.0]).reshape(3, 1, 1)
      )
  
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 21 for calling the MLModelTest obj's underlying member method code with example usage: obj.test_rename_image_input() without return types