code: def forward(
      self,
      batch_idx: int,
      batch: tensor_dict_type,
      state: Optional[TrainerState] = None,
      *,
      return_encodings: bool = False,
      **kwargs: Any,
  ) -> tensor_dict_type:
      batch = shallow_copy_dict(batch)
      encodings = run_encoder(
          self.encoder1d, batch_idx, batch, state, **kwargs
      )
      if return_encodings:
          return encodings
      if self.head is not None:
          return {PREDICTIONS_KEY: self.head(encodings[LATENT_KEY])}
      results = {}
      for key, head in self.heads.items():
          predictions = head(encodings[key])
          results[
              PREDICTIONS_KEY if key == LATENT_KEY else key
          ] = predictions
      return results
  
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 2 for calling the VanillaClassifier obj's underlying member method code with example usage: obj.forward(batch_idx, batch, state) and returns: results, encodings, dict