code: def test_mrpc(
      dispatch_batches: bool = False, split_batches: bool = False
  ):
      metric = evaluate.load("glue", "mrpc")
      setup, accelerator = get_mrpc_setup(
          dispatch_batches, split_batches
      )
      # First do baseline
      model, dataloader, device = setup["no"]
      model.to(device)
      model.eval()
      for batch in dataloader:
          batch.to(device)
          with torch.inference_mode():
              outputs = model(**batch)
          preds = outputs.logits.argmax(dim=-1)
          metric.add_batch(
              predictions=preds, references=batch["labels"]
          )
      baseline = metric.compute()
  
      # Then do distributed
      model, dataloader, device = setup["ddp"]
      model.eval()
      for batch in dataloader:
          with torch.inference_mode():
              outputs = model(**batch)
          preds = outputs.logits.argmax(dim=-1)
          references = batch["labels"]
          preds, references = accelerator.gather_for_metrics(
              (preds, references)
          )
          metric.add_batch(predictions=preds, references=references)
      distributed = metric.compute()
  
      for key in "accuracy f1".split():
          assert math.isclose(
              baseline[key], distributed[key]
          ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
  
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 6 for calling a global function with example usage: test_mrpc(dispatch_batches, split_batches) without return types