class_docstr: Lunar Multi-headed attention.
  See "Linformer: Self-Attention with Linear Complexity" for more details.
docstr: Reorder buffered internal state (for incremental generation).
docstr_tok: ["Reorder","buffered","internal","state","(","for","incremental","generation",")","."]
code: def reorder_incremental_state(
      self,
      incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
      new_order: Tensor,
  ):
      """Reorder buffered internal state (for incremental generation)."""
      input_buffer = self._get_input_buffer(incremental_state)
      if input_buffer is not None:
          for k in input_buffer.keys():
              input_buffer_k = input_buffer[k]
              if input_buffer_k is not None:
                  if (
                      self.encoder_decoder_attention
                      and input_buffer_k.size(0) == new_order.size(0)
                  ):
                      break
                  input_buffer[k] = input_buffer_k.index_select(
                      0, new_order
                  )
          incremental_state = self._set_input_buffer(
              incremental_state, input_buffer
          )
      return incremental_state
  
imports: import math
  from typing import Dict, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from fairseq import utils
  from fairseq.incremental_decoding_utils import with_incremental_state
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from torch import nn, Tensor
label_desc: Use image node_id 8 for calling the LunarMultiheadAttention obj's underlying member method code with example usage: obj.reorder_incremental_state(incremental_state, new_order) and returns: incremental_state