code: def forward(self, x):
      *_, height, width, heads, wsz, device = (
          *x.shape,
          self.heads,
          self.window_size,
          x.device,
      )
  
      # prenorm
  
      x = self.norm(x)
  
      # rearrange for short or long distance attention
  
      if self.attn_type == "short":
          x = rearrange(
              x, "b d (h s1) (w s2) -> (b h w) d s1 s2", s1=wsz, s2=wsz
          )
      elif self.attn_type == "long":
          x = rearrange(
              x, "b d (l1 h) (l2 w) -> (b h w) d l1 l2", l1=wsz, l2=wsz
          )
  
      # 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 (h d) x y -> b h (x y) d", h=heads),
          (q, k, v),
      )
      q = q * self.scale
  
      sim = einsum("b h i d, b h j d -> b h i j", q, k)
  
      # add dynamic positional bias
  
      pos = torch.arange(-wsz, wsz + 1, device=device)
      rel_pos = torch.stack(torch.meshgrid(pos, pos, indexing="ij"))
      rel_pos = rearrange(rel_pos, "c i j -> (i j) c")
      biases = self.dpb(rel_pos.float())
      rel_pos_bias = biases[self.rel_pos_indices]
  
      sim = sim + rel_pos_bias
  
      # attend
  
      attn = sim.softmax(dim=-1)
      attn = self.dropout(attn)
  
      # merge heads
  
      out = einsum("b h i j, b h j d -> b h i d", attn, v)
      out = rearrange(out, "b h (x y) d -> b (h d) x y", x=wsz, y=wsz)
      out = self.to_out(out)
  
      # rearrange back for long or short distance attention
  
      if self.attn_type == "short":
          out = rearrange(
              out,
              "(b h w) d s1 s2 -> b d (h s1) (w s2)",
              h=height // wsz,
              w=width // wsz,
          )
      elif self.attn_type == "long":
          out = rearrange(
              out,
              "(b h w) d l1 l2 -> b d (l1 h) (l2 w)",
              h=height // wsz,
              w=width // wsz,
          )
  
      return out
  
imports: import torch
  from torch import nn, einsum
  from einops import rearrange
  from einops.layers.torch import Rearrange, Reduce
  import torch.nn.functional
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: out