code: def train(
      self,
      train_data: Optional[Union[str, DataFrame]] = None,
      validate_data: Optional[Union[str, DataFrame]] = None,
      output_dir: str = None,
      saved_model_path: str = None,
  ):
      if self.is_client():
          train_set = self._prepare_data(train_data, "train_data")
          validate_set = self._prepare_data(validate_data, "val_data")
          trainer = self.client_setup(
              train_set=train_set,
              validate_set=validate_set,
              output_dir=output_dir,
              saved_model=saved_model_path,
          )
          self.trainer = trainer
          trainer.train()
          if output_dir is not None:
              if (
                  self.training_args.deepspeed
                  and self.training_args.local_rank != 0
              ):
                  pass
              else:
                  trainer.save_model(output_dir)
      elif self.is_server():
          trainer = self.server_setup()
          trainer.train()
  
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 5 for calling the DefaultRunner obj's underlying member method code with example usage: obj.train(train_data, validate_data, output_dir, saved_model_path) without return types