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 get_intermediate_layers(self, x):
      x = self.patch_embed(x)
      batch_size, seq_len, _ = x.size()
  
      cls_tokens = self.cls_token.expand(
          batch_size, -1, -1
      )  # stole cls_tokens impl from Phil Wang, thanks
      x = torch.cat((cls_tokens, x), dim=1)
      if self.pos_embed is not None:
          x = x + self.pos_embed
      x = self.pos_drop(x)
  
      features = []
      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)
          features.append(x)
  
      return features
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  from timm.models.registry import register_model
  from lavis.common.dist_utils import download_cached_file
label_desc: Use image node_id 8 for calling the VisionTransformer obj's underlying member method code with example usage: obj.get_intermediate_layers(x) and returns: features