code: def forward(self, x):
      device, h, bias_indices = (
          x.device,
          self.heads,
          self.rel_pos_indices,
      )
  
      x = self.norm(x)
  
      # 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(bias_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)
  
      # combine heads out
  
      out = rearrange(out, "b h n d -> b n (h d)")
      return self.to_out(out)
  
imports: from functools import partial
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from torch.nn import Module, ModuleList, Sequential
  from einops import rearrange, repeat, reduce, pack, unpack
  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