code: def run_example():
      dataset = get_dataset("electricity")
      serialize_path = Path("GluonTSTabularPredictor")
      estimator = TabularEstimator(
          freq="H",
          prediction_length=24,
          time_limit=600,  # ten minutes for training
          disable_auto_regression=True,  # makes prediction faster, but potentially less accurate
          last_k_for_val=24,  # split the last 24 targets from each time series to be the validation data
          quantiles_to_predict=[0.1, 0.5, 0.9],
      )
  
      n_train = 5
  
      training_data = list(islice(dataset.train, n_train))
  
      predictor = estimator.train(training_data=training_data)
  
      os.makedirs(serialize_path, exist_ok=True)
      predictor.serialize(serialize_path)
      predictor = None
      # the quantiles_to_predict parameters should be List[str] type
      predictor = Predictor.deserialize(serialize_path)
      forecasts = list(predictor.predict(training_data))
      print(forecasts)
  
imports: from itertools import islice
  from pathlib import Path
  import os
  from gluonts.dataset.repository import get_dataset
  from gluonts.nursery.autogluon_tabular import TabularEstimator
  from gluonts.model.predictor import Predictor
label_desc: Use image node_id 1 for calling a global function with example usage: run_example() without return types