code: def forward(
      self, x: Tuple[torch.Tensor, torch.Tensor]
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      x, cls_tokens = x
      token_length = cls_tokens.shape[1]
      if self.pool is not None:
          x, cls_tokens = self.pool(x, cls_tokens)
  
      B, C, H, W = x.shape
      x = x.flatten(2).transpose(1, 2)
      x = torch.cat((cls_tokens, x), dim=1)
  
      x = self.norm(x)
      x = self.blocks(x)
  
      cls_tokens = x[:, :token_length]
      x = x[:, token_length:]
      x = x.transpose(1, 2).reshape(B, C, H, W)
  
      return x, cls_tokens
  
imports: import math
  import re
  from functools import partial
  from typing import Sequence, Tuple
  import torch
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_, to_2tuple, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(x) and returns: x,  cls_tokens