code: def get_lm_model(args, device, config):
      """Get language model(based on GPT-2) used for sequence prediction."""
  
      ninp = config["ninp"]
      nhead = config["nhead"]
      initrange = config["initrange"]
      dropout = config["dropout"]
      vocab_size = config["vocab_size"]
      nhid = config["nhid"]
      ndecoder = config["num_decoder_layers"]
  
      return transformer_lm.TransformerLM(
          vocab_size, ninp, nhead, nhid, dropout, initrange, ndecoder
      ).to(device)
  
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 5 for calling a global function with example usage: get_lm_model(args, device, config) and returns: transformer_lm