from tensorflow.contrib.summary import summary_test_util

    dev_data = data.SnliData(fake_train_file, word2index)
    test_data = data.SnliData(fake_train_file, word2index)

    # 2. Create a fake config.
    config = _test_spinn_config(
        data.WORD_VECTOR_LEN, 4,
        logdir=os.path.join(self._temp_data_dir, "logdir"))

    # 3. Test training of a SPINN model.
    trainer = spinn.train_or_infer_spinn(
        embed, word2index, train_data, dev_data, test_data, config)

    # 4. Load train loss values from the summary files and verify that they
    #    decrease with training.
    summary_file = glob.glob(os.path.join(config.logdir, "events.out.*"))[0]
    events = summary_test_util.events_from_file(summary_file)
    train_losses = [event.summary.value[0].simple_value for event in events
                    if event.summary.value
                    and event.summary.value[0].tag == "train/loss"]
    self.assertEqual(config.epochs, len(train_losses))
    self.assertLess(train_losses[-1], train_losses[0])

    # 5. Verify that checkpoints exist and contains all the expected variables.
    self.assertTrue(glob.glob(os.path.join(config.logdir, "ckpt*")))
    ckpt_variable_names = [
        item[0] for item in checkpoint_utils.list_variables(config.logdir)]
    self.assertIn("global_step", ckpt_variable_names)
    for v in trainer.variables:
      variable_name = v.name[:v.name.index(":")] if ":" in v.name else v.name
      self.assertIn(variable_name, ckpt_variable_names)
