code: def test(
      self,
      use_cpu_only,
      backend,
      op,
      upsample_factor,
      data_format,
      interpolation,
  ):
      kwargs = {}
      shape = None
      if op == tf.keras.layers.UpSampling1D:
          shape = np.random.randint(low=2, high=4, size=3)
          upsample_factor = upsample_factor[2]
      elif op == tf.keras.layers.UpSampling2D:
          kwargs = {
              "data_format": data_format,
              "interpolation": interpolation,
          }
          shape = np.random.randint(low=2, high=4, size=4)
          upsample_factor = (upsample_factor[1], upsample_factor[2])
      elif op == tf.keras.layers.UpSampling3D:
          kwargs = {"data_format": data_format}
          shape = np.random.randint(low=2, high=4, size=5)
  
      model = tf.keras.Sequential(
          [op(batch_input_shape=shape, size=upsample_factor, **kwargs)]
      )
      run_compare_tf_keras(
          model,
          [random_gen(shape, rand_min=-10, rand_max=10)],
          use_cpu_only=use_cpu_only,
          backend=backend,
      )
  
imports: import random
  from coremltools.converters.mil import testing_reqs
  from coremltools.converters.mil.frontend.tensorflow2.test.testing_utils import run_compare_tf_keras
  from coremltools.converters.mil.testing_reqs import *
label_desc: Use image node_id 1 for calling the TestUpSampling obj's underlying member method code with example usage: obj.test(use_cpu_only, backend, op, upsample_factor, data_format, interpolation) without return types