code: def xformers_attention_forward(
      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)
  
      q, k, v = (
          rearrange(t, "b n (h d) -> b n h 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()
  
      out = xformers.ops.memory_efficient_attention(
          q,
          k,
          v,
          attn_bias=None,
          op=get_xformers_flash_attention_op(q, k, v),
      )
  
      out = out.to(dtype)
  
      out = rearrange(out, "b n h d -> b n (h d)", h=h)
      return self.to_out(out)
  
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 17 for calling a global function with example usage: xformers_attention_forward(x, context, mask) and returns: self