code: def onnx_forward(self, batch: tensor_dict_type) -> Any:
      results = self.classify(batch[INPUT_KEY], determinate=True)
      return {k: softmax(v) for k, v in results.items()}
  
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 3 for calling the VanillaClassifier obj's underlying member method code with example usage: obj.onnx_forward(batch) and returns: unknown