code: def forward(self, x: Tensor) -> Tensor:
      qkv = self.to_qkv(x).chunk(3, dim=-1)
  
      b, p, n, hd = qkv[0].shape
      q, k, v = (
          t.reshape(b, p, n, self.heads, hd // self.heads).transpose(
              2, 3
          )
          for t in qkv
      )
  
      dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
      attn = self.attend(dots)
      out = torch.matmul(attn, v)
      out = out.transpose(2, 3).reshape(b, p, n, hd)
      return self.to_out(out)
  
imports: from typing import Any, Dict, Tuple
  import torch
  from torch import nn
  from kornia.core import Module, Tensor
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: self