code: def prediction_step(
      self,
      model: nn.Module,
      inputs: Dict[str, Union[torch.Tensor, Any]],
      prediction_loss_only: bool,
      ignore_keys: Optional[List[str]] = None,
  ):
      # (features, labels), this format is used in FATE-1.x
      # now the model is in eval status
      if isinstance(inputs, tuple) or isinstance(inputs, list):
          inputs = self._prepare_inputs(inputs)
          with torch.no_grad():
              if len(inputs) == 2:  # data & label
                  feats, labels = inputs
                  output = model(feats)
              if len(inputs) == 1:  # label only
                  labels = inputs[0]
                  output = model()
          return None, output, labels
      else:
          return super().prediction_step(
              model, inputs, prediction_loss_only, ignore_keys
          )
  
imports: import torch
  from dataclasses import dataclass, fields
  from enum import Enum
  from torch import nn
  from typing import Any, Dict, List, Union, Callable, Literal
  from fate.arch import Context
  from torch.utils.data import Dataset
  from transformers import PreTrainedTokenizer
  from transformers import EvalPrediction
  from transformers.trainer_callback import TrainerCallback
  from typing import Optional
  from fate.ml.nn.model_zoo.hetero_nn_model import HeteroNNModelGuest, HeteroNNModelHost
  from fate.ml.nn.trainer.trainer_base import HeteroTrainerBase, TrainingArguments
  from fate.ml.nn.model_zoo.hetero_nn_model import StdAggLayerArgument, FedPassArgument, SSHEArgument
  from fate.ml.nn.model_zoo.hetero_nn_model import TopModelStrategyArguments
label_desc: Use image node_id 4 for calling the HeteroNNTrainerGuest obj's underlying member method code with example usage: obj.prediction_step(model, inputs, prediction_loss_only, ignore_keys) and returns: None,  output,  labels, super