code: def forward(self, q, k, v, mask=None):
      attn = torch.bmm(q, k.permute(0, 2, 1))  # query-key overlap
  
      if self.scale:
          dimension = torch.as_tensor(
              k.size(-1), dtype=attn.dtype, device=attn.device
          ).sqrt()
          attn = attn / dimension
  
      if mask is not None:
          attn = attn.masked_fill(mask, -1e9)
      attn = self.softmax(attn)
  
      if self.dropout is not None:
          attn = self.dropout(attn)
      output = torch.bmm(attn, v)
      return output, attn
  
imports: import math
  from typing import Dict, List, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the ScaledDotProductAttention obj's underlying member method code with example usage: obj.forward(q, k, v, mask) and returns: output,  attn