code: def forward_features(self, x):
      # [B, C, H, W] -> [B, num_patches, embed_dim]
      x = self.patch_embed(x)  # [B, 196, 768]
      # [1, 1, 768] -> [B, 1, 768]
      cls_token = self.cls_token.expand(x.shape[0], -1, -1)
      if self.dist_token is None:
          x = torch.cat((cls_token, x), dim=1)  # [B, 197, 768]
      else:
          x = torch.cat(
              (
                  cls_token,
                  self.dist_token.expand(x.shape[0], -1, -1),
                  x,
              ),
              dim=1,
          )
  
      x = self.pos_drop(x + self.pos_embed)
      x = self.blocks(x)
      x = self.norm(x)
      if self.dist_token is None:
          return self.pre_logits(x[:, 0])
      else:
          return x[:, 0], x[:, 1]
  
imports: from functools import partial
  from collections import OrderedDict
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the VisionTransformer obj's underlying member method code with example usage: obj.forward_features(x) and returns: self, x,  x