code: def predict(
      self,
      n: int,
      num_samples: int = 1,
      verbose: bool = False,
      show_warnings: bool = True,
  ):
      super().predict(n, num_samples)
  
      predictions_with_observations = np.concatenate(
          (
              self.rolling_window,
              np.zeros(shape=(n, self.rolling_window.shape[1])),
          ),
          axis=0,
      )
      rolling_sum = sum(self.rolling_window)
  
      chunk_length = self.input_chunk_length
      for i in range(chunk_length, chunk_length + n):
          prediction = rolling_sum / chunk_length
          predictions_with_observations[i] = prediction
          lost_value = predictions_with_observations[i - chunk_length]
          rolling_sum += prediction - lost_value
      return self._build_forecast_series(
          predictions_with_observations[-n:]
      )
  
imports: from typing import List, Optional, Sequence, Union
  import numpy
  from darts.logging import get_logger, raise_if, raise_if_not
  from darts.models.forecasting.ensemble_model import EnsembleModel
  from darts.models.forecasting.forecasting_model import ForecastingModel, LocalForecastingModel
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 6 for calling the NaiveMovingAverage obj's underlying member method code with example usage: obj.predict(n, num_samples, verbose, show_warnings) and returns: self