code: def forward(self, x: torch.Tensor):
      x = self.conv1(x)  # shape = [*, width, grid, grid]
      x = x.reshape(
          x.shape[0], x.shape[1], -1
      )  # shape = [*, width, grid ** 2]
      x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
      x = torch.cat(
          [
              self.class_embedding.to(x.dtype)
              + torch.zeros(
                  x.shape[0],
                  1,
                  x.shape[-1],
                  dtype=x.dtype,
                  device=x.device,
              ),
              x,
          ],
          dim=1,
      )  # shape = [*, grid ** 2 + 1, width]
      x = x + self.positional_embedding.to(x.dtype)
      x = self.ln_pre(x)
  
      x = x.permute(1, 0, 2)  # NLD -> LND
      x = self.transformer(x)
      x = x.permute(1, 0, 2)  # LND -> NLD
  
      x = self.ln_post(x[:, 0, :])
  
      if self.proj is not None:
          x = x @ self.proj
  
      return x
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the VisionTransformer obj's underlying member method code with example usage: obj.forward(x) and returns: x