code: def get_dataloader(accelerator: Accelerator, use_longest=False):
      tokenizer = AutoTokenizer.from_pretrained(
          "hf-internal-testing/mrpc-bert-base-cased"
      )
      dataset = load_dataset("glue", "mrpc", split="validation")
  
      def tokenize_function(examples):
          outputs = tokenizer(
              examples["sentence1"],
              examples["sentence2"],
              truncation=True,
              max_length=None,
          )
          return outputs
  
      with accelerator.main_process_first():
          tokenized_datasets = dataset.map(
              tokenize_function,
              batched=True,
              remove_columns=["idx", "sentence1", "sentence2"],
          )
  
      tokenized_datasets = tokenized_datasets.rename_column(
          "label", "labels"
      )
  
      def collate_fn(examples):
          if use_longest:
              return tokenizer.pad(
                  examples, padding="longest", return_tensors="pt"
              )
          return tokenizer.pad(
              examples,
              padding="max_length",
              max_length=128,
              return_tensors="pt",
          )
  
      return DataLoader(
          tokenized_datasets,
          shuffle=False,
          collate_fn=collate_fn,
          batch_size=16,
      )
  
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 2 for calling a global function with example usage: get_dataloader(accelerator, use_longest) and returns: DataLoader, outputs, tokenizer, tokenizer