def get_test_tfdataset(self, test_dataset: tf.data.Dataset) -> tf.data.Dataset:
    '\n        Returns a test :class:`~tf.data.Dataset`.\n\n        Args:\n            test_dataset (:class:`~tf.data.Dataset`):\n                The dataset to use. The dataset should yield tuples of ``(features, labels)`` where ``features`` is\n                a dict of input features and ``labels`` is the labels. If ``labels`` is a tensor, the loss is\n                calculated by the model by calling ``model(features, labels=labels)``. If ``labels`` is a dict, such\n                as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated\n                by calling ``model(features, **labels)``.\n\n        Subclass and override this method if you want to inject some custom behavior.\n        '
    num_examples = tf.data.experimental.cardinality(test_dataset).numpy()
    if (num_examples < 0):
        raise ValueError('The training dataset must have an asserted cardinality')
    approx = (math.floor if self.args.dataloader_drop_last else math.ceil)
    steps = approx((num_examples / self.args.eval_batch_size))
    ds = test_dataset.repeat().batch(self.args.eval_batch_size, drop_remainder=self.args.dataloader_drop_last).prefetch(tf.data.experimental.AUTOTUNE)
    return (self.args.strategy.experimental_distribute_dataset(ds), steps, num_examples)
Returns a test :class:`~tf.data.Dataset`.

Args:
    test_dataset (:class:`~tf.data.Dataset`):
        The dataset to use. The dataset should yield tuples of ``(features, labels)`` where ``features`` is
        a dict of input features and ``labels`` is the labels. If ``labels`` is a tensor, the loss is
        calculated by the model by calling ``model(features, labels=labels)``. If ``labels`` is a dict, such
        as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated
        by calling ``model(features, **labels)``.

Subclass and override this method if you want to inject some custom behavior.
def get_test_tfdataset(self, test_dataset: tf.data.Dataset) -> tf.data.Dataset:
    '\n        Returns a test :class:`~tf.data.Dataset`.\n\n        Args:\n            test_dataset (:class:`~tf.data.Dataset`):\n                The dataset to use. The dataset should yield tuples of ``(features, labels)`` where ``features`` is\n                a dict of input features and ``labels`` is the labels. If ``labels`` is a tensor, the loss is\n                calculated by the model by calling ``model(features, labels=labels)``. If ``labels`` is a dict, such\n                as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated\n                by calling ``model(features, **labels)``.\n\n        Subclass and override this method if you want to inject some custom behavior.\n        '
    num_examples = tf.data.experimental.cardinality(test_dataset).numpy()
    if (num_examples < 0):
        raise ValueError('The training dataset must have an asserted cardinality')
    approx = (math.floor if self.args.dataloader_drop_last else math.ceil)
    steps = approx((num_examples / self.args.eval_batch_size))
    ds = test_dataset.repeat().batch(self.args.eval_batch_size, drop_remainder=self.args.dataloader_drop_last).prefetch(tf.data.experimental.AUTOTUNE)
    return (self.args.strategy.experimental_distribute_dataset(ds), steps, num_examples)