code: def forward(self, x, context=None):
      b, n, _, h = *x.shape, self.heads
  
      x = self.norm(x)
      context = (
          x if not exists(context) else torch.cat((x, context), dim=1)
      )
  
      qkv = (self.to_q(x), *self.to_kv(context).chunk(2, dim=-1))
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), qkv
      )
  
      dots = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
  
      dots = einsum(
          "b h i j, h g -> b g i j", dots, self.mix_heads_pre_attn
      )  # talking heads, pre-softmax
  
      attn = self.attend(dots)
      attn = self.dropout(attn)
  
      attn = einsum(
          "b h i j, h g -> b g i j", attn, self.mix_heads_post_attn
      )  # talking heads, post-softmax
  
      out = einsum("b h i j, b h j d -> b h i d", attn, v)
      out = rearrange(out, "b h n d -> b n (h d)")
      return self.to_out(out)
  
imports: from random import randrange
  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, context) and returns: self