code: def client_setup(
      self,
      train_set=None,
      validate_set=None,
      output_dir=None,
      saved_model=None,
      stage="train",
  ):
      if stage == "predict":
          self.local_mode = True
  
      if self.algo == "fedavg":
          client_class: FedAVGClient = FedAVG.client
      else:
          raise ValueError(f"algo {self.algo} not supported")
  
      ctx = self.get_context()
      model = loader_load_from_conf(self.model_conf)
      if model is None:
          raise ValueError(
              f"model is None, cannot load model from conf {self.model_conf}"
          )
  
      if output_dir is None:
          output_dir = "./"
  
      resume_path = None
      if saved_model is not None:
          model_dict = load_model_dict_from_path(saved_model)
          model.load_state_dict(model_dict)
          logger.info(
              f"loading model dict from {saved_model} to model done"
          )
          if get_last_checkpoint(saved_model) is not None:
              resume_path = saved_model
              logger.info(
                  f"checkpoint detected, resume_path set to {resume_path}"
              )
      # load optimizer
      if self.optimizer_conf:
          optimizer_loader = Loader.from_dict(self.optimizer_conf)
          optimizer_ = optimizer_loader.load_item()
          optimizer_params = optimizer_loader.kwargs
          optimizer = optimizer_(model.parameters(), **optimizer_params)
      else:
          optimizer = None
      # load loss
      loss = (
          loader_load_from_conf(self.loss_conf)
          if self.loss_conf
          else None
      )
      # load collator func
      data_collator = loader_load_from_conf(self.data_collator_conf)
      # load tokenizer if import conf provided
      tokenizer = loader_load_from_conf(self.tokenizer_conf)
      # args
      dir_warning(self.training_args_conf)
      training_args = TrainingArguments(**self.training_args_conf)
      self.training_args = training_args
      # reset to default, saving to arbitrary path is not allowed in
      # DefaultRunner
      training_args.output_dir = output_dir
      training_args.resume_from_checkpoint = resume_path  # resume path
      fed_args = FedAVGArguments(**self.fed_args_conf)
  
      # prepare trainer
      trainer = client_class(
          ctx=ctx,
          model=model,
          loss_fn=loss,
          optimizer=optimizer,
          training_args=training_args,
          fed_args=fed_args,
          data_collator=data_collator,
          tokenizer=tokenizer,
          train_set=train_set,
          val_set=validate_set,
          local_mode=self.local_mode,
      )
  
      _check_instances(
          trainer=trainer,
          model=model,
          optimizer=optimizer,
          loss=loss,
          train_args=training_args,
          fed_args=fed_args,
          data_collator=data_collator,
      )
      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 3 for calling the DefaultRunner obj's underlying member method code with example usage: obj.client_setup(train_set, validate_set, output_dir, saved_model, stage) and returns: trainer