code: def main(cfg):
      logging.info(f"Hydra config: {OmegaConf.to_yaml(cfg)}")
  
      trainer = pl.Trainer(**cfg.trainer)
      exp_manager(trainer, cfg.get("exp_manager", None))
      model = EncDecFrameClassificationModel(
          cfg=cfg.model, trainer=trainer
      )
  
      # Initialize the weights of the model from another model, if provided via config
      model.maybe_init_from_pretrained_checkpoint(cfg)
  
      trainer.fit(model)
  
      if (
          hasattr(cfg.model, "test_ds")
          and cfg.model.test_ds.manifest_filepath is not None
      ):
          if model.prepare_test(trainer):
              trainer.test(model)
  
imports: import pytorch_lightning
  from omegaconf import OmegaConf
  from nemo.collections.asr.models.classification_models import EncDecFrameClassificationModel
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
  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