code: def forward(self, x):
      B, N, C = x.shape
      qkv = (
          self.qkv(x)
          .reshape(B, N, 3, self.num_heads, -1)
          .permute(2, 0, 3, 4, 1)
      )
      q, k, v = qkv.unbind(0)
  
      # NOTE, this is NOT spatial attn, q, k, v are B, num_heads, C, L -->  C x C attn map
      attn = (
          F.normalize(q, dim=-1)
          @ F.normalize(k, dim=-1).transpose(-2, -1)
      ) * self.temperature
      attn = attn.softmax(dim=-1)
      attn = self.attn_drop(attn)
      x = attn @ v
  
      x = x.permute(0, 3, 1, 2).reshape(B, N, C)
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d, use_fused_attn, NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the CrossCovarianceAttn obj's underlying member method code with example usage: obj.forward(x) and returns: x