code: def _linear_bw(
      dy: torch.Tensor, x: torch.Tensor, bias: bool
  ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
      if not bias:
          return (dy.transpose(-2, -1) @ x), None
      db = torch.empty([dy.shape[1]], dtype=dy.dtype, device=dy.device)
      dw = torch.empty(
          [dy.shape[1], x.shape[1]], dtype=dy.dtype, device=dy.device
      )
      GemmFusedSumOp.OPERATOR(dy.transpose(-2, -1), x, dw, db)
      return dw, db
  
imports: from dataclasses import dataclass
  from typing import Dict, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from .common import BaseOperator, get_xformers_operator, register_operator
  from .unbind import stack_or_none, unbind
label_desc: Use image node_id 2 for calling the _SwiGLUFusedFunc obj's underlying member method code with example usage: obj._linear_bw(dy, x, bias) and returns: dw,  db, unknown,  None