def _preprocess_fids(self, fids, configs):
    if (fids.indices.shape.rank == 2):
        fids = tf.IndexedSlices(indices=fids.indices[:, 0], values=fids.values, dense_shape=fids.dense_shape)
    features = {}
    for config in configs:
        features.update(operator._multidevice_preprocess_fids(fids, config, num_shards=self._num_shards))
    return features
def _preprocess_fids(self, fids, configs):
    if (fids.indices.shape.rank == 2):
        fids = tf.IndexedSlices(indices=fids.indices[:, 0], values=fids.values, dense_shape=fids.dense_shape)
    features = {}
    for config in configs:
        features.update(operator._multidevice_preprocess_fids(fids, config, num_shards=self._num_shards))
    return features