code: def forward(self, *args, is_training=False, **kwargs):
      ret = self.forward_features(*args, **kwargs)
      if is_training:
          return ret
      else:
          return self.head(ret["x_norm_clstoken"])
  
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 10 for calling the DinoVisionTransformer obj's underlying member method code with example usage: obj.forward() and returns: ret, self