code: def run_decoder(
      decoder: IDecoder,
      batch_idx: int,
      batch: tensor_dict_type,
      state: Optional[TrainerState] = None,
      **kwargs: Any,
  ) -> tensor_dict_type:
      return _forward(
          decoder, batch_idx, batch, INPUT_KEY, state, **kwargs
      )
  
imports: from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import Type
  from typing import Optional
  from cftool.misc import safe_execute
  from cftool.misc import print_warning
  from cftool.misc import shallow_copy_dict
  from cftool.misc import WithRegister
  from cftool.types import tensor_dict_type
  from ....schema import _forward
  from ....schema import TrainerState
  from ....constants import INPUT_KEY
  from ....constants import LABEL_KEY
  from ....constants import PREDICTIONS_KEY
  from ....misc.toolkit import interpolate
  from ....misc.toolkit import auto_num_layers
  from ....modules.blocks import ChannelPadding
label_desc: Use image node_id 2 for calling a global function with example usage: run_decoder(decoder, batch_idx, batch, state) and returns: _forward