code: def forward(self, net: Tensor, **kwargs: Any) -> tensor_dict_type:
      latent = super().forward(net, **kwargs)
      rs = {LATENT_KEY: latent}
      if self.aux_heads is None:
          if self.output_projection is not None:
              rs[LATENT_KEY] = latent @ self.output_projection
      else:
          for i, k in enumerate(self.aux_heads + [LATENT_KEY]):
              rs[k] = latent[:, i]
      return rs
  
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 ViTEncoder obj's underlying member method code with example usage: obj.forward(net) and returns: rs