code: def attention(self, h_: torch.Tensor) -> torch.Tensor:
      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 = map(
          lambda x: rearrange(x, "b c h w -> b 1 (h w) c").contiguous(),
          (q, k, v),
      )
      h_ = torch.nn.functional.scaled_dot_product_attention(
          q, k, v
      )  # scale is dim ** -0.5 per default
      # compute attention
  
      return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
  
imports: import logging
  import math
  from typing import Any, Callable, Optional
  import numpy
  import torch
  import torch.nn
  from einops import rearrange
  from packaging import version
  from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
label_desc: Use image node_id 2 for calling the AttnBlock obj's underlying member method code with example usage: obj.attention(h_) and returns: rearrange