code: def forward(self, x, context=None, mask=None):
      h, has_context = self.heads, exists(context)
  
      context = default(context, x)
  
      if has_context and self.cross_attn_include_queries:
          context = torch.cat((x, context), dim=-2)
  
      q, k, v = (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), (q, k, v)
      )
  
      out = self.attend(q, k, v, mask=mask)
  
      out = rearrange(out, "b h n d -> b n (h d)")
      return self.to_out(out)
  
imports: from collections import namedtuple
  from functools import wraps
  import torch
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
  from packaging import version
  from torch import einsum, nn
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, context, mask) and returns: self