code: def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
      B, N, C = x.shape
      qkv = (
          self.qkv(x)
          .reshape(B, N, 3, self.num_heads, self.head_dim)
          .permute(2, 0, 3, 1, 4)
      )
      q, k, v = qkv.unbind(0)
      q = self.q_norm(q)
      k = self.k_norm(k)
  
      if self.fused_attn:
          if self.rel_pos is not None:
              attn_bias = self.rel_pos.get_bias()
          elif shared_rel_pos is not None:
              attn_bias = shared_rel_pos
          else:
              attn_bias = None
  
          x = torch.nn.functional.scaled_dot_product_attention(
              q,
              k,
              v,
              attn_mask=attn_bias,
              dropout_p=self.attn_drop.p if self.training else 0.0,
          )
      else:
          q = q * self.scale
          attn = q @ k.transpose(-2, -1)
          if self.rel_pos is not None:
              attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
          elif shared_rel_pos is not None:
              attn = attn + shared_rel_pos
          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 logging
  import math
  from functools import partial
  from typing import Optional, Tuple
  import torch
  import torch.nn
  from torch.jit import Final
  from torch.utils.checkpoint import checkpoint
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, RelPosMlp, RelPosBias, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 2 for calling the RelPosAttention obj's underlying member method code with example usage: obj.forward(x, shared_rel_pos) and returns: x