code: def setup_trainer(
      cfg,
      train_dataset,
      eval_dataset,
      model,
      tokenizer,
      total_num_steps,
  ):
      if cfg.rl:
          trainer_builder = HFDPOTrainerBuilder(
              cfg, model[0], tokenizer
          )
          trainer_builder.model_ref = model[1]
      else:
          trainer_builder = HFCausalTrainerBuilder(
              cfg, model[0], tokenizer
          )
  
      trainer_builder.train_dataset = train_dataset
      trainer_builder.eval_dataset = eval_dataset
  
      return trainer_builder.build(total_num_steps)
  
imports: import math
  import os
  from contextlib import contextmanager
  from functools import partial
  from typing import List
  import numpy
  import torch
  import torch.cuda
  from accelerate.logging import get_logger
  from datasets import set_caching_enabled
  from torch.utils.data import DataLoader, RandomSampler
  from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFDPOTrainerBuilder
  from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first
  from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
label_desc: Use image node_id 13 for calling a global function with example usage: setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps) and returns: trainer_builder