code: def __init__(
      self,
      dim,
      num_heads=8,
      qkv_bias=False,
      qk_norm=False,
      rel_pos_cls=None,
      attn_drop=0.0,
      proj_drop=0.0,
      norm_layer=nn.LayerNorm,
  ):
      super().__init__()
      assert (
          dim % num_heads == 0
      ), "dim should be divisible by num_heads"
      self.num_heads = num_heads
      self.head_dim = dim // num_heads
      self.scale = self.head_dim**-0.5
      self.fused_attn = use_fused_attn()
  
      self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
      self.q_norm = (
          norm_layer(self.head_dim) if qk_norm else nn.Identity()
      )
      self.k_norm = (
          norm_layer(self.head_dim) if qk_norm else nn.Identity()
      )
      self.rel_pos = (
          rel_pos_cls(num_heads=num_heads) if rel_pos_cls else None
      )
      self.attn_drop = nn.Dropout(attn_drop)
      self.proj = nn.Linear(dim, dim)
      self.proj_drop = nn.Dropout(proj_drop)
  
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 1 to create a new RelPosAttention object from inherited base classes: nn with example: obj = RelPosAttention(dim, num_heads, qkv_bias, qk_norm, rel_pos_cls, attn_drop, proj_drop, norm_layer)