code: def get_real_dataloaders(args, device, benchmark_config, model_specs):
      """Returns dataloaders for real data."""
  
      if args.model_name == "lm":
          data = get_real_wikitext2_dataloaders(
              args, benchmark_config, model_specs
          )
          (
              ntokens,
              train_dataloader,
              valid_dataloader,
              test_dataloader,
          ) = data
          model_specs["vocab_size"] = ntokens
          return train_dataloader, valid_dataloader, test_dataloader
      else:
          raise RuntimeError(
              "Unrecognized args.model_mame " % args.model_name
          )
  
imports: import argparse
  from collections import defaultdict
  from functools import reduce
  import gc
  import logging
  import math
  import operator
  import time
  from datasets.wikitext2_data import get_real_dataloaders
  from datasets.wikitext2_data import get_synthetic_dataloaders
  from models import transformer_lm
  import numpy
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Adam
  from benchmarks.golden_configs.lm_wikitext2 import FSDP
  from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 14 for calling a global function with example usage: get_real_dataloaders(args, device, benchmark_config, model_specs) and returns: train_dataloader,  valid_dataloader,  test_dataloader