code: def create_model_config(
      args, benchmark_config=None, model_specs=None
  ):
      """Return a dict with the given model, dataset and optimizer."""
  
      device = (
          torch.device("cuda")
          if torch.cuda.is_available()
          else torch.device("cpu")
      )
  
      if args.use_synthetic_data:
          dataloader_fn = get_synthetic_dataloaders
      else:
          dataloader_fn = get_real_dataloaders
  
      data = dataloader_fn(args, device, benchmark_config, model_specs)
      model, optimizer = get_model_and_optimizer(
          args, device, benchmark_config, model_specs
      )
      return {
          "model": model,
          "optimizer": optimizer,
          "data": data,
      }
  
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 15 for calling a global function with example usage: create_model_config(args, benchmark_config, model_specs) and returns: dict