code: def __init__(
      self,
      prediction_length: int,
      freq: str,
      predictors: List[Predictor],
      aggregation_method: Optional[str] = "median",
  ) -> None:
      super().__init__(prediction_length, freq)
  
      assert aggregation_method in AGGREGATION_METHODS
  
      self.predictors = predictors
      self.aggregation_method = aggregation_method
  
imports: import copy
  import logging
  from itertools import product
  from typing import List, Optional, Iterator
  import numpy
  from gluonts.core.component import validated
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.common import Dataset
  from gluonts.model.predictor import Predictor
  from gluonts.model.forecast import Forecast, SampleForecast
  from pts.model import PyTorchEstimator
  from pts import Trainer
  from .n_beats_estimator import NBEATSEstimator
  from .n_beats_network import VALID_LOSS_FUNCTIONS
label_desc: Use image node_id 1 to create a new NBEATSEnsemblePredictor object from inherited base classes: Predictor with example: obj = NBEATSEnsemblePredictor(prediction_length, freq, predictors, aggregation_method)