answering).
Tips:
This implementation is the same as [RobertaModel] with the addition of entity embeddings as well
  as an entity-aware self-attention mechanism, which improves performance on tasks involving reasoning about entities.
LUKE treats entities as input tokens; therefore, it takes entity_ids, entity_attention_mask,
  entity_token_type_ids and entity_position_ids as extra input. You can obtain those using
  [LukeTokenizer].
[LukeTokenizer] takes entities and entity_spans (character-based start and end
  positions of the entities in the input text) as extra input. entities typically consist of [MASK] entities or
  Wikipedia entities. The brief description when inputting these entities are as follows:
Inputting [MASK] entities to compute entity representations: The [MASK] entity is used to mask entities to be
