code: def dispatch_dense_model(name: str, **kwargs):
      (
          model_class,
          model_basename,
          model_layer,
          *model_kwargs,
      ) = DENSE_MODELS[name]
      # Add other model arguments to kwargs (i.e. {'feat_hop_size': 640})
      for mkwargs in model_kwargs:
          for k, v in mkwargs.items():
              # don't overwrite keys in kwargs
              if k not in kwargs:
                  kwargs[k] = v
      checkpoint_path = CHECKPOINT_MANAGER.get_by_name(model_basename)
      return model_class(checkpoint_path, layer=model_layer, **kwargs)
  
file_path: textlesslib/textless/__init__.py
imports: from textless.data.cpc_feature_reader import CpcFeatureReader
  from textless.data.hubert_feature_reader import HubertFeatureReader
  from textless.data.kmeans_quantizer import KMeansQuantizer
  from textless.checkpoint_manager import CHECKPOINT_MANAGER
  from textless.vocoders.tacotron2.vocoder import TacotronVocoder
  from textless.vocoders.hifigan.vocoder import CodeHiFiGANVocoder
label_desc: Use image node_id 1 for calling a global function with example usage: dispatch_dense_model(name) and returns: model_class