deep_ep/buffer.py [232:240]:
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                 handle: Optional[Tuple] = None,
                 num_tokens_per_rank: Optional[torch.Tensor] = None, num_tokens_per_rdma_rank: Optional[torch.Tensor] = None,
                 is_token_in_rank: Optional[torch.Tensor] = None, num_tokens_per_expert: Optional[torch.Tensor] = None,
                 topk_idx: Optional[torch.Tensor] = None, topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1,
                 config: Optional[Config] = None,
                 previous_event: Optional[EventOverlap] = None, async_finish: bool = False,
                 allocate_on_comm_stream: bool = False) -> \
            Tuple[Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor], Optional[torch.Tensor],
                  Optional[torch.Tensor], List[int], Tuple, EventOverlap]:
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deep_ep/buffer.py [352:360]:
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                           handle: Optional[Tuple] = None,
                           num_tokens_per_rank: Optional[torch.Tensor] = None, num_tokens_per_rdma_rank: Optional[torch.Tensor] = None,
                           is_token_in_rank: Optional[torch.Tensor] = None, num_tokens_per_expert: Optional[torch.Tensor] = None,
                           topk_idx: Optional[torch.Tensor] = None, topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1,
                           config: Optional[Config] = None,
                           previous_event: Optional[EventOverlap] = None, async_finish: bool = False,
                           allocate_on_comm_stream: bool = False) -> \
            Tuple[Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor], Optional[torch.Tensor],
            Optional[torch.Tensor], List[int], Tuple, EventOverlap]:
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