code: def forward_features_list(self, x_list, masks_list):
      x = [
          self.prepare_tokens_with_masks(x, masks)
          for x, masks in zip(x_list, masks_list)
      ]
      for blk in self.blocks:
          x = blk(x)
  
      all_x = x
      output = []
      for x, masks in zip(all_x, masks_list):
          x_norm = self.norm(x)
          output.append(
              {
                  "x_norm_clstoken": x_norm[:, 0],
                  "x_norm_regtokens": x_norm[
                      :, 1 : self.num_register_tokens + 1
                  ],
                  "x_norm_patchtokens": x_norm[
                      :, self.num_register_tokens + 1 :
                  ],
                  "x_prenorm": x,
                  "masks": masks,
              }
          )
      return output
  
imports: from functools import partial
  import math
  import logging
  from typing import Sequence, Tuple, Union, Callable
  import torch
  import torch.nn
  import torch.utils.checkpoint
  from torch.nn.init import trunc_normal_
  from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock
label_desc: Use image node_id 5 for calling the DinoVisionTransformer obj's underlying member method code with example usage: obj.forward_features_list(x_list, masks_list) and returns: output