code: def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
      """Instantiate a transformer LM."""
      if cfg.lm_model in ["transformer_lm", "transformer_lm_magnet"]:
          kwargs = dict_from_config(getattr(cfg, "transformer_lm"))
          n_q = kwargs["n_q"]
          q_modeling = kwargs.pop("q_modeling", None)
          codebooks_pattern_cfg = getattr(cfg, "codebooks_pattern")
          attribute_dropout = dict_from_config(
              getattr(cfg, "attribute_dropout")
          )
          cls_free_guidance = dict_from_config(
              getattr(cfg, "classifier_free_guidance")
          )
          cfg_prob, cfg_coef = (
              cls_free_guidance["training_dropout"],
              cls_free_guidance["inference_coef"],
          )
          fuser = get_condition_fuser(cfg)
          condition_provider = get_conditioner_provider(
              kwargs["dim"], cfg
          ).to(cfg.device)
          if (
              len(fuser.fuse2cond["cross"]) > 0
          ):  # enforce cross-att programmatically
              kwargs["cross_attention"] = True
          if codebooks_pattern_cfg.modeling is None:
              assert (
                  q_modeling is not None
              ), "LM model should either have a codebook pattern defined or transformer_lm.q_modeling"
              codebooks_pattern_cfg = omegaconf.OmegaConf.create(
                  {
                      "modeling": q_modeling,
                      "delay": {"delays": list(range(n_q))},
                  }
              )
  
          pattern_provider = get_codebooks_pattern_provider(
              n_q, codebooks_pattern_cfg
          )
          lm_class = (
              MagnetLMModel
              if cfg.lm_model == "transformer_lm_magnet"
              else LMModel
          )
          return lm_class(
              pattern_provider=pattern_provider,
              condition_provider=condition_provider,
              fuser=fuser,
              cfg_dropout=cfg_prob,
              cfg_coef=cfg_coef,
              attribute_dropout=attribute_dropout,
              dtype=getattr(torch, cfg.dtype),
              device=cfg.device,
              **kwargs,
          ).to(cfg.device)
      else:
          raise KeyError(f"Unexpected LM model {cfg.lm_model}")
  
imports: import typing
  import audiocraft
  import omegaconf
  import torch
  from .encodec import CompressionModel, EncodecModel, InterleaveStereoCompressionModel
  from .lm import LMModel
  from .lm_magnet import MagnetLMModel
  from ..modules.codebooks_patterns import CodebooksPatternProvider, DelayedPatternProvider, MusicLMPattern, ParallelPatternProvider, UnrolledPatternProvider, CoarseFirstPattern
  from ..modules.conditioners import BaseConditioner, ChromaStemConditioner, CLAPEmbeddingConditioner, ConditionFuser, ConditioningProvider, LUTConditioner, T5Conditioner
  from .unet import DiffusionUnet
  from ..None import quantization
  from ..utils.utils import dict_from_config
  from ..modules.diffusion_schedule import MultiBandProcessor, SampleProcessor
label_desc: Use image node_id 4 for calling a global function with example usage: get_lm_model(cfg) and returns: lm_class