code: def test_parallelized_predictor():
      dataset = ListDataset(
          data_iter=[
              {
                  "start": "2012-01-01",
                  "target": (np.zeros(20) + i).tolist(),
              }
              for i in range(300)
          ],
          freq="1H",
      )
  
      base_predictor = IdentityPredictor(
          prediction_length=10, num_samples=100
      )
  
      predictor = ParallelizedPredictor(
          base_predictor=base_predictor, num_workers=10, chunk_size=2
      )
  
      predictions = list(base_predictor.predict(dataset))
      parallel_predictions = list(predictor.predict(dataset))
  
      assert len(predictions) == len(parallel_predictions)
  
      for p, pp in zip(predictions, parallel_predictions):
          assert np.all(p.samples == pp.samples)
          assert np.all(p.index == pp.index)
  
imports: import numpy
  from gluonts.dataset.common import ListDataset
  from gluonts.evaluation import backtest_metrics
  from gluonts.model.predictor import Localizer, ParallelizedPredictor
  from gluonts.model.trivial.identity import IdentityPredictor
  from gluonts.model.trivial.mean import MeanEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_parallelized_predictor() without return types