code: def forward(self, x, feat_size: List[int]):
      B, N, C = x.shape
      H, W = feat_size
      q = (
          self.q(x)
          .reshape(B, N, self.num_heads, -1)
          .permute(0, 2, 1, 3)
      )
  
      if self.pool is not None:
          x = x.permute(0, 2, 1).reshape(B, C, H, W)
          x = self.sr(self.pool(x)).reshape(B, C, -1).permute(0, 2, 1)
          x = self.norm(x)
          x = self.act(x)
          kv = (
              self.kv(x)
              .reshape(B, -1, 2, self.num_heads, self.head_dim)
              .permute(2, 0, 3, 1, 4)
          )
      else:
          if self.sr is not None:
              x = x.permute(0, 2, 1).reshape(B, C, H, W)
              x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
              x = self.norm(x)
              kv = (
                  self.kv(x)
                  .reshape(B, -1, 2, self.num_heads, self.head_dim)
                  .permute(2, 0, 3, 1, 4)
              )
          else:
              kv = (
                  self.kv(x)
                  .reshape(B, -1, 2, self.num_heads, self.head_dim)
                  .permute(2, 0, 3, 1, 4)
              )
      k, v = kv.unbind(0)
  
      if self.fused_attn:
          x = F.scaled_dot_product_attention(
              q,
              k,
              v,
              dropout_p=self.attn_drop.p if self.training else 0.0,
          )
      else:
          q = q * self.scale
          attn = q @ k.transpose(-2, -1)
          attn = attn.softmax(dim=-1)
          attn = self.attn_drop(attn)
          x = attn @ v
  
      x = x.transpose(1, 2).reshape(B, N, C)
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: import math
  from typing import Tuple, List, Callable, Union
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, to_ntuple, trunc_normal_, LayerNorm, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, feat_size) and returns: x