code: def backward(ctx, dw):
      q, k, w, mask = ctx.saved_tensors
      max_neg_value = -torch.finfo(q.dtype).max
      s = (
          dw.detach()
          .norm(float("inf"), dim=[1, 2], keepdim=True)
          .clip(min=1e-4)
      )
      dw = dw / s
      db = torch._softmax_backward_data(
          grad_output=dw, output=w, dim=2, input_dtype=dw.dtype
      )
  
      # Masking db
      db_in = db.clone().masked_fill_(mask, 0)
  
      s = s / math.sqrt(k.shape[1])
      dq = torch.einsum("nck,nqk->ncq", k, db_in) * s
      dk = torch.einsum("ncq,nqk->nck", q, db_in) * s
  
      # These are dummy derivatives since mask is a constant
      dmask = (max_neg_value - w) * db.clone() * s
  
      return dq, dk, dmask
  
imports: import math
  import numpy
  import torch
  import torch.nn
  from torch.cuda.amp import custom_bwd, custom_fwd
label_desc: Use image node_id 2 for calling the StableMaskedAttentionOp obj's underlying member method code with example usage: obj.backward(ctx, dw) and returns: dq,  dk,  dmask