code: def create_dataloaders(args):
      ds_kwargs = {"streaming": True}
      train_data = load_dataset(
          args.dataset_name_train, split="train", **ds_kwargs
      )
      train_data = train_data.shuffle(
          buffer_size=args.shuffle_buffer, seed=args.seed
      )
      valid_data = load_dataset(
          args.dataset_name_valid, split="train", **ds_kwargs
      )
      train_dataset = ConstantLengthDataset(
          tokenizer,
          train_data,
          infinite=True,
          seq_length=args.seq_length,
          tokenized=args.tokenized,
      )
      valid_dataset = ConstantLengthDataset(
          tokenizer,
          valid_data,
          infinite=False,
          seq_length=args.seq_length,
          tokenized=args.tokenized,
      )
      train_dataset = train_dataset.shuffle(
          buffer_size=args.shuffle_buffer
      )
      train_dataloader = DataLoader(
          train_dataset, batch_size=args.train_batch_size, shuffle=True
      )
      eval_dataloader = DataLoader(
          valid_dataset, batch_size=args.valid_batch_size
      )
      return train_dataloader, eval_dataloader
  
imports: import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  import datasets
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  import transformers
  from accelerate import Accelerator, DistributedType
  from arguments import TrainingArguments
  from huggingface_hub import Repository
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: create_dataloaders(args) and returns: train_dataloader,  eval_dataloader