import tensorflow as tf

            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
            return f

        if feature:
            features = collections.OrderedDict()
            features["input_ids"] = create_int_feature(feature.input_ids)
            features["input_mask"] = create_int_feature(feature.input_mask)
            features["segment_ids"] = create_int_feature(feature.segment_ids)
            features["label_ids"] = create_int_feature(feature.label_id)
            features["is_real_example"] = create_int_feature(
                [int(feature.is_real_example)])

            tf_example = tf.train.Example(features=tf.train.Features(feature=features))
            writer.write(tf_example.SerializeToString())
    writer.close()


def file_based_input_fn_builder(input_file, seq_length, is_training,
                                drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
