code: def get_model_and_optimizer(
      args, device, benchmark_config, model_config
  ):
      """Return instantiated model and optimizer function."""
  
      if args.model_name == "lm":
          model = get_lm_model(args, device, model_config)
  
      lr = benchmark_config["lr"]
  
      def make_adam(params):
          return Adam(params, lr=lr)
  
      optimizer = make_adam
      return model, optimizer
  
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 4 for calling a global function with example usage: get_model_and_optimizer(args, device, benchmark_config, model_config) and returns: model,  optimizer, Adam