code: def __init__(
      self,
      dim,
      num_heads,
      mlp_ratio=4.0,
      qkv_bias=False,
      qk_norm=False,
      rel_pos_cls=None,
      init_values=None,
      proj_drop=0.0,
      attn_drop=0.0,
      drop_path=0.0,
      act_layer=nn.GELU,
      norm_layer=nn.LayerNorm,
  ):
      super().__init__()
      self.norm1 = norm_layer(dim)
      self.attn = RelPosAttention(
          dim,
          num_heads,
          qkv_bias=qkv_bias,
          qk_norm=qk_norm,
          rel_pos_cls=rel_pos_cls,
          attn_drop=attn_drop,
          proj_drop=proj_drop,
      )
      self.ls1 = (
          LayerScale(dim, init_values=init_values)
          if init_values
          else nn.Identity()
      )
      # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
      self.drop_path1 = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
  
      self.norm2 = norm_layer(dim)
      self.mlp = Mlp(
          in_features=dim,
          hidden_features=int(dim * mlp_ratio),
          act_layer=act_layer,
          drop=proj_drop,
      )
      self.ls2 = (
          LayerScale(dim, init_values=init_values)
          if init_values
          else nn.Identity()
      )
      self.drop_path2 = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
  
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 RelPosBlock object from inherited base classes: nn with example: obj = RelPosBlock(dim, num_heads, mlp_ratio, qkv_bias, qk_norm, rel_pos_cls, init_values, proj_drop, attn_drop, drop_path, act_layer, norm_layer)