code: def forward(self, x) -> torch.tensor:
      x = self._process_input(x)
      n = x.shape[0]
  
      # Expand the class token to the full batch
      batch_class_token = self.class_token.expand(n, -1, -1)
      x = torch.cat([batch_class_token, x], dim=1)
      x = self.model.encoder(x)
  
      # mean of all encoder outputs, performs better than the classifier "token" as used by standard language
      # architectures
      x = x.mean(dim=1)
      return x
  
imports: from PIL.Image import Image
  from typing import Callable, NamedTuple, Optional
  import torch
  import torch.nn
  from torchvision import models
  from torchvision.transforms import transforms
  from torchvision.models import vit_b_16, EfficientNet_B4_Weights
  from torchvision.models.vision_transformer import ViT_B_16_Weights
label_desc: Use image node_id 3 for calling the ViT obj's underlying member method code with example usage: obj.forward(x) and returns: x