code: def forward(ctx, q, k, mask):
      max_neg_value = -float("inf")
      w = torch.einsum("ncq,nck->nqk", q, k / math.sqrt(k.shape[1]))
      w = w.masked_fill(mask, max_neg_value)
      w = w.softmax(dim=2)
  
      # When we use an arbitrary mask, there is a possibility that we get nans in softmax.
      # In this case, use nan_to_num to make it a stable number.
      w = w.nan_to_num_()
      ctx.save_for_backward(q, k, w, mask)
      return w
  
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 1 for calling the StableMaskedAttentionOp obj's underlying member method code with example usage: obj.forward(ctx, q, k, mask) and returns: w