code: def split_cross_attention_forward_v1(
      self, x, context=None, mask=None, **kwargs
  ):
      h = self.heads
  
      q_in = self.to_q(x)
      context = default(context, x)
  
      context_k, context_v = hypernetwork.apply_hypernetworks(
          shared.loaded_hypernetworks, context
      )
      k_in = self.to_k(context_k)
      v_in = self.to_v(context_v)
      del context, context_k, context_v, x
  
      q, k, v = (
          rearrange(t, "b n (h d) -> (b h) n d", h=h)
          for t in (q_in, k_in, v_in)
      )
      del q_in, k_in, v_in
  
      dtype = q.dtype
      if shared.opts.upcast_attn:
          q, k, v = q.float(), k.float(), v.float()
  
      with devices.without_autocast(
          disable=not shared.opts.upcast_attn
      ):
          r1 = torch.zeros(
              q.shape[0],
              q.shape[1],
              v.shape[2],
              device=q.device,
              dtype=q.dtype,
          )
          for i in range(0, q.shape[0], 2):
              end = i + 2
              s1 = einsum("b i d, b j d -> b i j", q[i:end], k[i:end])
              s1 *= self.scale
  
              s2 = s1.softmax(dim=-1)
              del s1
  
              r1[i:end] = einsum("b i j, b j d -> b i d", s2, v[i:end])
              del s2
          del q, k, v
  
      r1 = r1.to(dtype)
  
      r2 = rearrange(r1, "(b h) n d -> b n (h d)", h=h)
      del r1
  
      return self.to_out(r2)
  
imports: from __future__ import annotations
  import math
  import psutil
  import platform
  import torch
  from torch import einsum
  from ldm.util import default
  from einops import rearrange
  from modules import shared, errors, devices, sub_quadratic_attention
  from modules.hypernetworks import hypernetwork
  import ldm.modules.attention
  import ldm.modules.diffusionmodules.model
  import sgm.modules.attention
  import sgm.modules.diffusionmodules.model
label_desc: Use image node_id 3 for calling a global function with example usage: split_cross_attention_forward_v1(x, context, mask) and returns: self