class_docstr: Vision Transformer with support for patch or hybrid CNN input stage
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","or","hybrid","CNN","input","stage"]
code: def forward_features(self, x):
      x = self.prepare_tokens(x)
  
      rel_pos_bias = (
          self.rel_pos_bias() if self.rel_pos_bias is not None else None
      )
      for blk in self.blocks:
          x = blk(x, rel_pos_bias=rel_pos_bias)
  
      x = self.norm(x)
      return x[:, 0]
  
imports: import copy
  import logging
  import math
  from functools import partial
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 6 for calling the BEiT obj's underlying member method code with example usage: obj.forward_features(x) and returns: x