code: def forward(self, x):
      shape = x.shape
      b, n, _, y, h = *shape, self.heads
  
      x = self.norm(x)
      q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim=1))
      q, k, v = map(
          lambda t: rearrange(t, "b (h d) x y -> (b h) (x y) d", h=h),
          (q, k, v),
      )
  
      dots = einsum("b i d, b j d -> b i j", q, k) * self.scale
  
      attn = self.attend(dots)
      attn = self.dropout(attn)
  
      out = einsum("b i j, b j d -> b i d", attn, v)
      out = rearrange(out, "(b h) (x y) d -> b (h d) x y", h=h, y=y)
      return self.to_out(out)
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
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