class_docstr: Vision Transformer.
  
  A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
      - https://arxiv.org/abs/2010.11929
  
  Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
      - https://arxiv.org/abs/2012.12877
class_docstr_tok: ["Vision","Transformer",".","A","PyTorch","impl","of",":","`","An","Image","is","Worth","16x16","Words",":","Transformers","for","Image","Recognition","at","Scale","`","-","https",":","\/\/arxiv.org\/abs\/2010.11929","Includes","distillation","token","&","head","support","for","`","DeiT",":","Data-efficient","Image","Transformers","`","-","https",":","\/\/arxiv.org\/abs\/2012.12877"]
code: def forward_features(self, x):
      x, H, W = self.patch_embed(x)
      cls_token = self.cls_token.expand(
          x.shape[0], -1, -1
      )  # stole cls_tokens impl from Phil Wang, thanks
      x = torch.cat((cls_token, x), dim=1)
      x = self.pos_drop(x + self.pos_embed)
  
      # For CLIP
      x = self.norm_pre(x)
  
      for blk in self.blocks:
          x = blk(x, H, W)
      x = self.norm(x)
      return x
  
imports: import logging
  import math
  from functools import partial
  from itertools import repeat
  from typing import Callable, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.runner import BaseModule, load_checkpoint
  from mmseg.ops import resize
  from mmseg.utils import get_root_logger
  from torch import Tensor
  from .drop_path import DropPath
label_desc: Use image node_id 3 for calling the TIMMVisionTransformer obj's underlying member method code with example usage: obj.forward_features(x) and returns: x