code: def forward(self, x):
      (
          batch,
          height,
          width,
          window_height,
          window_width,
          _,
          device,
          h,
      ) = (*x.shape, x.device, self.heads)
  
      x = self.norm(x)
  
      # flatten
  
      x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d")
  
      # project for queries, keys, values
  
      q, k, v = self.to_qkv(x).chunk(3, dim=-1)
  
      # split heads
  
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)
      )
  
      # scale
  
      q = q * self.scale
  
      # sim
  
      sim = einsum("b h i d, b h j d -> b h i j", q, k)
  
      # add positional bias
  
      bias = self.rel_pos_bias(self.rel_pos_indices)
      sim = sim + rearrange(bias, "i j h -> h i j")
  
      # attention
  
      attn = self.attend(sim)
  
      # aggregate
  
      out = einsum("b h i j, b h j d -> b h i d", attn, v)
  
      # merge heads
  
      out = rearrange(
          out,
          "b h (w1 w2) d -> b w1 w2 (h d)",
          w1=window_height,
          w2=window_width,
      )
  
      # combine heads out
  
      out = self.to_out(out)
      return rearrange(
          out, "(b x y) ... -> b x y ...", x=height, y=width
      )
  
imports: from functools import partial
  import torch
  from torch import nn, einsum
  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: rearrange