code: def main(cfg: DictConfig) -> None:
      logging.info(f"Config Params:\n {OmegaConf.to_yaml(cfg)}")
  
      # initialize the model using the config file
      if cfg.pretrained_model and os.path.exists(cfg.pretrained_model):
          model = ZeroShotIntentModel.restore_from(
              cfg.pretrained_model, strict=False
          )
      else:
          raise ValueError(
              "Provide path to the pre-trained .nemo checkpoint"
          )
  
      # predicting an intent of a query
      queries = [
          "I'd like a veggie burger and fries",
          "Turn off the lights in the living room",
      ]
  
      candidate_labels = [
          "Food order",
          "Play music",
          "Request for directions",
          "Change lighting",
          "Calendar query",
      ]
  
      predictions = model.predict(
          queries, candidate_labels, batch_size=4, multi_label=True
      )
  
      logging.info(
          "The prediction results of some sample queries with the trained model:"
      )
      for query in predictions:
          logging.info(json.dumps(query, indent=4))
      logging.info("Inference finished!")
  
imports: import json
  import os
  from omegaconf import DictConfig, OmegaConf
  from nemo.collections.nlp.models import ZeroShotIntentModel
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: main(cfg) without return types