code: def testCelebaMultiResolutionPreprocessExample(self, resize_method):
      example = {
          "inputs": tf.random_uniform([218, 178, 3], minval=-1.0)
      }
      mode = tf_estimator.ModeKeys.TRAIN
      hparams = hparam.HParams(resolutions=[8, 16, 32])
      if resize_method is not None:
          hparams.resize_method = resize_method
  
      problem = celeba.ImageCelebaMultiResolution()
      preprocessed_example = problem.preprocess_example(
          example, mode, hparams
      )
      self.assertLen(preprocessed_example, 2)
      self.assertEqual(
          preprocessed_example["inputs"].shape, (138, 138, 3)
      )
      self.assertEqual(
          preprocessed_example["targets"].shape, (42, 32, 3)
      )
  
file_path: tensor2tensor/tensor2tensor/data_generators/celeba_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  from tensor2tensor.data_generators import celeba
  from tensor2tensor.utils import hparam
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling the CelebaTest obj's underlying member method code with example usage: obj.testCelebaMultiResolutionPreprocessExample(resize_method) without return types