code: def forward(self, x, context=None, kv_include_self=False):
      b, n, _, h = *x.shape, self.heads
      x = self.norm(x)
      context = default(context, x)
  
      if kv_include_self:
          context = torch.cat(
              (x, context), dim=1
          )  # cross attention requires CLS token includes itself as key / value
  
      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
  
      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 n d -> b n (h d)")
      return self.to_out(out)
  
imports: 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, kv_include_) and returns: self