[converter] implement torch's `aten::scaled_dot_product_attention` operator

Is there any chance to implement torch `aten::scaled_dot_product_attention`?

https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html says it could be done as
```
# Efficient implementation equivalent to the following:
scale_factor = 1 / math.sqrt(Q.size(-1)) if scale is None else scale
attn_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0) if is_causal else attn_mask
attn_mask = attn_mask.masked_fill(not attn_mask, -float('inf')) if attn_mask.dtype==torch.bool else attn_mask
attn_weight = torch.softmax((Q @ K.transpose(-2, -1) * scale_factor) + attn_mask, dim=-1)
attn_weight = torch.dropout(attn_weight, dropout_p)
return attn_weight @ V
```

Locate the converter's existing PyTorch operator handling and compare its conventions with the linked PyTorch scaled_dot_product_attention documentation. Verify completion against the documented behavior for scale, causal and explicit masks, softmax, dropout, and the final value projection, using any nearby operator tests that the repository exposes.

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