code: def cross_attention_attnblock_forward(self, x):
      h_ = x
      h_ = self.norm(h_)
      q1 = self.q(h_)
      k1 = self.k(h_)
      v = self.v(h_)
  
      # compute attention
      b, c, h, w = q1.shape
  
      q2 = q1.reshape(b, c, h * w)
      del q1
  
      q = q2.permute(0, 2, 1)  # b,hw,c
      del q2
  
      k = k1.reshape(b, c, h * w)  # b,c,hw
      del k1
  
      h_ = torch.zeros_like(k, device=q.device)
  
      mem_free_total = get_available_vram()
  
      tensor_size = (
          q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
      )
      mem_required = tensor_size * 2.5
      steps = 1
  
      if mem_required > mem_free_total:
          steps = 2 ** (
              math.ceil(math.log(mem_required / mem_free_total, 2))
          )
  
      slice_size = (
          q.shape[1] // steps
          if (q.shape[1] % steps) == 0
          else q.shape[1]
      )
      for i in range(0, q.shape[1], slice_size):
          end = i + slice_size
  
          w1 = torch.bmm(
              q[:, i:end], k
          )  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
          w2 = w1 * (int(c) ** (-0.5))
          del w1
          w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
          del w2
  
          # attend to values
          v1 = v.reshape(b, c, h * w)
          w4 = w3.permute(
              0, 2, 1
          )  # b,hw,hw (first hw of k, second of q)
          del w3
  
          h_[:, :, i:end] = torch.bmm(
              v1, w4
          )  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
          del v1, w4
  
      h2 = h_.reshape(b, c, h, w)
      del h_
  
      h3 = self.proj_out(h2)
      del h2
  
      h3 += x
  
      return h3
  
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 20 for calling a global function with example usage: cross_attention_attnblock_forward(x) and returns: h3