code: def predict(
      self,
      n: int,
      num_samples: int = 1,
      verbose: bool = False,
      show_warnings: bool = True,
  ):
      super().predict(n, num_samples)
  
      if num_samples == 1:
          forecast = self.model.forecast(n)
      else:
          forecast = np.expand_dims(
              self.model.simulate(n, repetitions=num_samples), axis=1
          )
  
      return self._build_forecast_series(forecast)
  
file_path: darts/darts/models/forecasting/exponential_smoothing.py
imports: from typing import Any, Dict, Optional
  import numpy
  import statsmodels.tsa.holtwinters
  from darts.logging import get_logger
  from darts.models.forecasting.forecasting_model import LocalForecastingModel
  from darts.timeseries import TimeSeries
  from darts.utils.utils import ModelMode, SeasonalityMode
label_desc: Use image node_id 3 for calling the ExponentialSmoothing obj's underlying member method code with example usage: obj.predict(n, num_samples, verbose, show_warnings) and returns: self