class_docstr: Vision Transformer as per https://arxiv.org/abs/2010.11929.
class_docstr_tok: ["Vision","Transformer","as","per","https",":","\/\/arxiv.org\/abs\/2010.11929","."]
code: def forward(self, x: torch.Tensor):
      # Reshape and permute the input 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.encoder(x)
  
      # Classifier "token" as used by standard language architectures
      x = x[:, 0]
  
      x = self.heads(x)
  
      return x
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Any, Callable, Dict, List, NamedTuple, Optional
  import torch
  import torch.nn
  from ..ops.misc import Conv2dNormActivation, MLP
  from ..transforms._presets import ImageClassification, InterpolationMode
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 3 for calling the VisionTransformer obj's underlying member method code with example usage: obj.forward(x) and returns: x