code: def get_mrpc_setup(dispatch_batches, split_batches):
      accelerator = Accelerator(
          dispatch_batches=dispatch_batches, split_batches=split_batches
      )
      dataloader = get_dataloader(accelerator, not dispatch_batches)
      model = AutoModelForSequenceClassification.from_pretrained(
          "hf-internal-testing/mrpc-bert-base-cased", return_dict=True
      )
      ddp_model, ddp_dataloader = accelerator.prepare(model, dataloader)
      return {
          "ddp": [ddp_model, ddp_dataloader, torch_device],
          "no": [model, dataloader, accelerator.device],
      }, accelerator
  
imports: import logging
  import math
  import os
  from copy import deepcopy
  import datasets
  import evaluate
  import torch
  import transformers
  from datasets import load_dataset
  from torch.utils.data import DataLoader, IterableDataset
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from accelerate import Accelerator
  from accelerate.data_loader import DataLoaderDispatcher
  from accelerate.test_utils import RegressionDataset, RegressionModel, torch_device
  from accelerate.utils import set_seed
label_desc: Use image node_id 3 for calling a global function with example usage: get_mrpc_setup(dispatch_batches, split_batches) and returns: dict,  accelerator