code: def forward(self, x):
      h = self.heads
      x, ps = pack_one(x, "b * d")
  
      x = self.norm(x)
      q, k, v = self.to_qkv(x).chunk(3, dim=-1)
  
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> b h d n", h=h), (q, k, v)
      )
  
      q, k = map(l2norm, (q, k))
  
      sim = (
          einsum("b h i n, b h j n -> b h i j", q, k)
          * self.temperature.exp()
      )
  
      attn = self.attend(sim)
      attn = self.dropout(attn)
  
      out = einsum("b h i j, b h j n -> b h i n", attn, v)
      out = rearrange(out, "b h d n -> b n (h d)")
  
      out = unpack_one(out, ps, "b * d")
      return self.to_out(out)
  
imports: from random import randrange
  import torch
  from torch import nn, einsum
  from torch.nn import Module, ModuleList
  import torch.nn.functional
  from einops import rearrange, repeat, pack, unpack
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling the XCAttention obj's underlying member method code with example usage: obj.forward(x) and returns: self