code: def forward(self, x):
      B, N, C = x.shape
      # B1C -> B1H(C/H) -> BH1(C/H)
      q = (
          self.wq(x[:, 0:1, ...])
          .reshape(B, 1, self.num_heads, C // self.num_heads)
          .permute(0, 2, 1, 3)
      )
      # BNC -> BNH(C/H) -> BHN(C/H)
      k = (
          self.wk(x)
          .reshape(B, N, self.num_heads, C // self.num_heads)
          .permute(0, 2, 1, 3)
      )
      # BNC -> BNH(C/H) -> BHN(C/H)
      v = (
          self.wv(x)
          .reshape(B, N, self.num_heads, C // self.num_heads)
          .permute(0, 2, 1, 3)
      )
  
      attn = (
          q @ k.transpose(-2, -1)
      ) * self.scale  # BH1(C/H) @ BH(C/H)N -> BH1N
      attn = attn.softmax(dim=-1)
      attn = self.attn_drop(attn)
  
      x = (
          (attn @ v).transpose(1, 2).reshape(B, 1, C)
      )  # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: from functools import partial
  from typing import List
  from typing import Tuple
  import torch
  import torch.hub
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 2 for calling the CrossAttention obj's underlying member method code with example usage: obj.forward(x) and returns: x