code: def __init__(
      self, loss_function: str, freq: str, *args, **kwargs
  ) -> None:
      super().__init__(*args, **kwargs)
      self.loss_function = loss_function
      self.freq = freq
  
      # Covert frequency string like "2H" to whats called the periodicity m.
      # E.g. 12 in case of "2H" because of 12 times two hours in a day.
      self.periodicity = get_seasonality(self.freq)
  
      if self.loss_function == "MASE":
          assert (
              self.periodicity
              < self.context_length + self.prediction_length
          ), (
              "If the 'periodicity' of your data is less than"
              " 'context_length' + 'prediction_length' the seasonal_error"
              " cannot be calculated and thus 'MASE' cannot be used for"
              " optimization."
          )
  
imports: from typing import List, Optional
  import mxnet
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
  from gluonts.time_feature import get_seasonality
label_desc: Use image node_id 1 to create a new NBEATSTrainingNetwork object from inherited base classes: NBEATSNetwork with example: obj = NBEATSTrainingNetwork(loss_function, freq)