class_docstr: This is just an example implementation with all
  operations explicited. This implementation is worse
  than pytorch, because pytorch is able to fuse some operations
  (eg the linear forward ...) that are decomposed here.
  
  The time measurements were made on the ViT-Giant setting:
  - A100/f16
  - input: [4440, 1536]
  - hidden: [4440, 4096]
class_docstr_tok: ["This","is","just","an","example","implementation","with","all","operations","explicited",".","This","implementation","is","worse","than","pytorch",",","because","pytorch","is","able","to","fuse","some","operations","(","eg","the","linear","forward","...",")","that","are","decomposed","here",".","The","time","measurements","were","made","on","the","ViT-Giant","setting",":","-","A100\/f16","-","input",":","[","4440",",","1536","]","-","hidden",":","[","4440",",","4096","]"]
code: def backward(cls, ctx, dx5):
      saved_tensors = ctx.saved_tensors
      if cls.FORCE_BW_F32:
          dx5 = dx5.float()
          saved_tensors = [t.float() for t in ctx.saved_tensors]
      x, w1, b1, w2, b2, w3, b3, x1, x2, x3, x4, x5 = saved_tensors
      dx4 = dx5 @ w3  # 255us (nn)
      dw3 = dx5.transpose(-2, -1) @ x4  # 247us (nt)
      db3 = dx5.sum(0)  # 25us
      dx3 = dx4 * x2  # 88us
      dx2 = dx4 * x3  # 88us
      dx1 = cls._silu_backward(dx3, x1)  # 90us
      dx = dx2 @ w2  # 260us (nn)
      dw2 = dx2.transpose(-2, -1) @ x  # 245us (nt)
      db2 = dx2.sum(0)  # 50us
      dx += dx1 @ w1  # 260us (nn)
      dw1 = dx1.transpose(-2, -1) @ x  # 245us (nt)
      db1 = dx1.sum(0)  # 50us
      return (dx, dw1, db1, dw2, db2, dw3, db3)
  
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 3 for calling the _SwiGLUDecomposedFunc obj's underlying member method code with example usage: obj.backward(cls, ctx, dx5) and returns: dx,  dw1,  db1,  dw2,  db2,  dw3,  db3