code: def sub_quad_attnblock_forward(self, x):
      h_ = x
      h_ = self.norm(h_)
      q = self.q(h_)
      k = self.k(h_)
      v = self.v(h_)
      b, c, h, w = q.shape
      q, k, v = (
          rearrange(t, "b c h w -> b (h w) c") for t in (q, k, v)
      )
      q = q.contiguous()
      k = k.contiguous()
      v = v.contiguous()
      out = sub_quad_attention(
          q,
          k,
          v,
          q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size,
          kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size,
          chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold,
          use_checkpoint=self.training,
      )
      out = rearrange(out, "b (h w) c -> b c h w", h=h)
      out = self.proj_out(out)
      return x + 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 24 for calling a global function with example usage: sub_quad_attnblock_forward(x) and returns: unknown