code: def main(cfg):
      # Generate default asr model config
      asr_model_config = configs.EncDecCTCModelConfig()
  
      # Merge hydra updates with model config
      # `drop_missing_subconfig=True` is necessary here. Without it, while the data class will instantiate and be added
      # to the config, it contains test_ds.sample_rate = MISSING and test_ds.labels = MISSING.
      # This will raise a OmegaConf MissingMandatoryValue error when processing the dataloaders inside
      # model_utils.resolve_test_dataloaders(model=self) (used for multi data loader support).
      # In general, any operation that tries to use a DictConfig with MISSING in it will fail,
      # other than explicit update operations to change MISSING to some actual value.
      asr_model_config = update_model_config(
          asr_model_config, cfg, drop_missing_subconfigs=True
      )
  
      # From here on out, its a general OmegaConf DictConfig, directly usable by our code.
      trainer = pl.Trainer(**asr_model_config.trainer)
      exp_manager(trainer, asr_model_config.get("exp_manager", None))
      asr_model = EncDecCTCModel(
          cfg=asr_model_config.model, trainer=trainer
      )
  
      trainer.fit(asr_model)
  
imports: import pytorch_lightning
  from nemo.collections.asr.models import EncDecCTCModel, configs
  from nemo.core.config import hydra_runner
  from nemo.utils.config_utils import update_model_config
  from nemo.utils.exp_manager import exp_manager
label_desc: Use image node_id 1 for calling a global function with example usage: main(cfg) without return types