class_docstr: Vision Transformer w/ Relative Position Bias
  
  Differing from classic vit, this impl
    * uses relative position index (swin v1 / beit) or relative log coord + mlp (swin v2) pos embed
    * defaults to no class token (can be enabled)
    * defaults to global avg pool for head (can be changed)
    * layer-scale (residual branch gain) enabled
class_docstr_tok: ["Vision","Transformer","w\/","Relative","Position","Bias","Differing","from","classic","vit",",","this","impl","*","uses","relative","position","index","(","swin","v1","\/","beit",")","or","relative","log","coord","+","mlp","(","swin","v2",")","pos","embed","*","defaults","to","no","class","token","(","can","be","enabled",")","*","defaults","to","global","avg","pool","for","head","(","can","be","changed",")","*","layer-scale","(","residual","branch","gain",")","enabled"]
code: def forward_head(self, x, pre_logits: bool = False):
      if self.global_pool:
          x = (
              x[:, self.num_prefix_tokens :].mean(dim=1)
              if self.global_pool == "avg"
              else x[:, 0]
          )
      x = self.fc_norm(x)
      x = self.head_drop(x)
      return x if pre_logits else self.head(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 9 for calling the VisionTransformerRelPos obj's underlying member method code with example usage: obj.forward_head(x, pre_logits) and returns: unknown