code: def forward(self, x):
      x = self.norm(x)
      qkv = self.to_qkv(x).chunk(3, dim=-1)
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads),
          qkv,
      )
  
      dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
  
      attn = self.attend(dots)
      attn = self.dropout(attn)
  
      out = torch.matmul(attn, v)
      out = rearrange(out, "b h n d -> b n (h d)")
      return self.to_out(out)
  
imports: import torch
  from torch import nn
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange, Reduce
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