code: def backward(ctx, dw):
      q, k, w = ctx.saved_tensors
      db = torch._softmax_backward_data(
          grad_output=dw.to(torch.float32),
          output=w.to(torch.float32),
          dim=2,
          input_dtype=torch.float32,
      )
      dq = torch.einsum("nck,nqk->ncq", k.to(torch.float32), db).to(
          q.dtype
      ) / np.sqrt(k.shape[1])
      dk = torch.einsum("ncq,nqk->nck", q.to(torch.float32), db).to(
          k.dtype
      ) / np.sqrt(k.shape[1])
      return dq, dk
  
imports: import torch
  import torch.nn
  import torchvision
  import numpy
  import torch
  from torch.nn.functional import silu
  from einops import rearrange, repeat
  from utils.general_utils import matrix_to_quaternion, quaternion_raw_multiply
  from utils.graphics_utils import fov2focal
label_desc: Use image node_id 2 for calling the AttentionOp obj's underlying member method code with example usage: obj.backward(ctx, dw) and returns: dq,  dk