code: def forward(self, net: Tensor, **kwargs: Any) -> Tensor:
      latent = super().forward(net, **kwargs)
      resolution = int(round(math.sqrt(self.num_patches)))
      latent = latent.view(latent.shape[0], resolution, resolution, -1)
      latent = latent.permute(0, 3, 1, 2).contiguous()
      return latent
  
file_path: carefree-learn/cflearn/models/cv/encoder/transformer.py
imports: import math
  import torch
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .schema import Encoder1DFromPatches
  from .schema import Encoder2DFromPatches
  from ....constants import LATENT_KEY
  from ....modules.blocks import MixedStackedEncoder
label_desc: Use image node_id 2 for calling the ViTEncoder2D obj's underlying member method code with example usage: obj.forward(net) and returns: latent