code: def forward(self, img, distill_token=None):
      distilling = exists(distill_token)
      x = self.to_patch_embedding(img)
      b, n, _ = x.shape
  
      cls_tokens = repeat(self.cls_token, "() n d -> b n d", b=b)
      x = torch.cat((cls_tokens, x), dim=1)
      x += self.pos_embedding[:, : (n + 1)]
  
      if distilling:
          distill_tokens = repeat(distill_token, "() n d -> b n d", b=b)
          x = torch.cat((x, distill_tokens), dim=1)
  
      x = self._attend(x)
  
      if distilling:
          x, distill_tokens = x[:, :-1], x[:, -1]
  
      x = x.mean(dim=1) if self.pool == "mean" else x[:, 0]
  
      x = self.to_latent(x)
      out = self.mlp_head(x)
  
      if distilling:
          return out, distill_tokens
  
      return out
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from vit_pytorch.vit import ViT
  from vit_pytorch.t2t import T2TViT
  from vit_pytorch.efficient import ViT
  from einops import rearrange, repeat
label_desc: Use image node_id 1 for calling the DistillMixin obj's underlying member method code with example usage: obj.forward(img, distill_token) and returns: out, out,  distill_tokens