code: def classify(self, net: Tensor, **kwargs: Any) -> tensor_dict_type:
      rs = self.forward(0, {INPUT_KEY: net}, **kwargs)
      if self.aux_keys is None:
          return {PREDICTIONS_KEY: rs[PREDICTIONS_KEY]}
      return {key: rs[key] for key in [PREDICTIONS_KEY] + self.aux_keys}
  
imports: import torch
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import Optional
  from cftool.misc import print_info
  from cftool.misc import shallow_copy_dict
  from cftool.array import softmax
  from cftool.types import tensor_dict_type
  from ..encoder import run_encoder
  from ..encoder import make_encoder
  from ....schema import IDLModel
  from ....schema import TrainerState
  from ....constants import INPUT_KEY
  from ....constants import LATENT_KEY
  from ....constants import PREDICTIONS_KEY
  from ....misc.toolkit import download_model
  from ....modules.blocks import Linear
label_desc: Use image node_id 4 for calling the VanillaClassifier obj's underlying member method code with example usage: obj.classify(net) and returns: unknown, dict