code: def predict(
      self,
      test_data: Union[str, DataFrame],
      saved_model_path: str = None,
  ) -> Union[DataFrame, None]:
      if self.is_client():
          test_set = self._prepare_data(test_data, "test_data")
          if self.trainer is not None:
              trainer = self.trainer
              logger.info("trainer found, skip setting up")
          else:
              trainer = self.client_setup(
                  saved_model=saved_model_path, stage="predict"
              )
  
          classes = run_dataset_func(test_set, "get_classes")
          match_ids = run_dataset_func(test_set, "get_match_ids")
          sample_ids = run_dataset_func(test_set, "get_sample_ids")
          match_id_name = run_dataset_func(
              test_set, "get_match_id_name"
          )
          sample_id_name = run_dataset_func(
              test_set, "get_sample_id_name"
          )
          pred_rs = trainer.predict(test_set)
  
          if (
              self.training_args
              and self.training_args.deepspeed
              and self.training_args.local_rank != 0
          ):
              return
  
          rs_df = self.get_nn_output_dataframe(
              self.get_context(),
              pred_rs.predictions,
              pred_rs.label_ids
              if hasattr(pred_rs, "label_ids")
              else None,
              match_ids,
              sample_ids,
              match_id_name=match_id_name,
              sample_id_name=sample_id_name,
              dataframe_format="fate_std",
              task_type=self.task_type,
              classes=classes,
          )
          return rs_df
      else:
          # server not predict
          return
  
imports: import torch
  import os
  from fate.components.components.nn.nn_runner import NNRunner, load_model_dict_from_path, dir_warning, loader_load_from_conf, run_dataset_func
  from fate.ml.nn.homo.fedavg import FedAVG, FedAVGArguments, FedAVGClient, FedAVGServer
  from typing import Dict
  from fate.components.components.nn.loader import Loader
  import torch.nn
  import torch.optim
  from torch.optim.lr_scheduler import _LRScheduler
  from fate.ml.nn.trainer.trainer_base import FedArguments, TrainingArguments, HomoTrainerClient, HomoTrainerServer
  from typing import Union, Type, Callable, Optional
  from transformers.trainer_utils import get_last_checkpoint
  from typing import Literal
  import logging
  from fate.components.components.utils import consts
  from fate.ml.nn.dataset.table import TableDataset, Dataset
  from fate.arch.dataframe import DataFrame
label_desc: Use image node_id 6 for calling the DefaultRunner obj's underlying member method code with example usage: obj.predict(test_data, saved_model_path) and returns: rs_df