code: def predict(
      self,
      n: int,
      num_samples: int = 1,
      verbose: bool = False,
      show_warnings: bool = True,
  ):
      super().predict(n, num_samples)
      trend_forecast = np.array(
          [
              self.trend_function(i + len(self.training_series))
              for i in range(n)
          ]
      )
      periodic_forecast = np.array(
          [
              self.predicted_values[i % len(self.predicted_values)]
              for i in range(n)
          ]
      )
      return self._build_forecast_series(
          periodic_forecast + trend_forecast
      )
  
imports: from typing import Callable, Optional
  import numpy
  import pandas
  from statsmodels.tsa.stattools import acf
  from darts.logging import get_logger
  from darts.models.forecasting.forecasting_model import LocalForecastingModel
  from darts.timeseries import TimeSeries
  from darts.utils.missing_values import fill_missing_values
label_desc: Use image node_id 7 for calling the FFT obj's underlying member method code with example usage: obj.predict(n, num_samples, verbose, show_warnings) and returns: self