code: def forward(self, x):
      b, n, *_, h = *x.shape, self.heads
  
      q = self.to_q(x)
      y = q.shape[2]
  
      qkv = (q, self.to_k(x), self.to_v(x))
      q, k, v = map(
          lambda t: rearrange(t, "b (h d) ... -> b h (...) d", h=h), qkv
      )
  
      dots = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
  
      dots = self.apply_pos_bias(dots)
  
      attn = self.attend(dots)
      attn = self.dropout(attn)
  
      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", h=h, y=y)
      return self.to_out(out)
  
imports: from math import ceil
  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 3 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: self