code: def test_resample(
      self,
      compute_unit,
      backend,
      data_warp_shapes,
  ):
      if backend[0] == "neuralnetwork":
          pytest.skip("nn backend not supported")
  
      tfa = pytest.importorskip("tensorflow_addons")
  
      data_shape, warp_shape = data_warp_shapes
  
      @make_tf_graph([data_shape, warp_shape])
      def build_model(x, warp):
          return tfa.image.resampler(data=x, warp=warp)
  
      model, inputs, outputs = build_model
      # warp exceeding input sizes in order to test more padding modes
      input_values = [
          random_gen(data_shape, -100, 100),
          random_gen(warp_shape, -15, 15),
      ]
      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 TestImageResample obj's underlying member method code with example usage: obj.test_resample(compute_unit, backend, data_warp_shapes) and returns: tfa