code: def server_setup(self, stage="train"):
      if stage == "predict":
          self.local_mode = True
      if self.algo == "fedavg":
          server_class: FedAVGServer = FedAVG.server
      else:
          raise ValueError(f"algo {self.algo} not supported")
      ctx = self.get_context()
      trainer = server_class(ctx=ctx, local_mode=self.local_mode)
      _check_instances(trainer)
      return trainer
  
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 4 for calling the DefaultRunner obj's underlying member method code with example usage: obj.server_setup(stage) and returns: trainer