code: def test(
      self, compute_unit, backend, transforms, interpolation, shapes
  ):
      x_shape, output_shape = shapes
      if backend[0] == "neuralnetwork":
          pytest.skip("nn backend not supported")
  
      tfa = pytest.importorskip("tensorflow_addons")
  
      @make_tf_graph([x_shape])
      def build_model(x):
          return tfa.image.transform(
              x,
              transforms=transforms,
              interpolation=interpolation,
              output_shape=output_shape,
          )
  
      model, inputs, outputs = build_model
      input_values = [
          random_gen(x_shape, -100, 100),
      ]
      input_dict = dict(zip(inputs, input_values))
      TensorFlowBaseTest.run_compare_tf(
          model,
          input_dict,
          outputs,
          compute_unit=compute_unit,
          backend=backend,
      )
  
imports: import itertools
  import numpy
  import pytest
  import coremltools
  from coremltools.converters.mil import testing_reqs
  from coremltools.converters.mil.frontend.tensorflow.test.testing_utils import TensorFlowBaseTest
  from coremltools.converters.mil.frontend.tensorflow2.test.testing_utils import TensorFlow2BaseTest
  from coremltools.converters.mil.frontend.tensorflow2.test.testing_utils import make_tf2_graph
  from coremltools.converters.mil.testing_utils import random_gen
label_desc: Use image node_id 1 for calling the TestImageTransform obj's underlying member method code with example usage: obj.test(compute_unit, backend, transforms, interpolation, shapes) and returns: tfa