class_docstr: Example:
      >>> #The dataset is split into data, validation_data
      >>> model = ARIMAForecaster(p=2, q=2, seasonality_mode=False)
      >>> model.fit(data, validation_data)
      >>> predict_result = model.predict(horizon=24)
class_docstr_tok: ["Example",":",">",">",">","#","The","dataset","is","split","into","data",",","validation_data",">",">",">","model","=","ARIMAForecaster","(","p=2",",","q=2",",","seasonality_mode=False",")",">",">",">","model.fit","(","data",",","validation_data",")",">",">",">","predict_result","=","model.predict","(","horizon=24",")"]
docstr: Save the forecaster.
  
  :param checkpoint_file: The location you want to save the forecaster.
docstr_tok: ["Save","the","forecaster",".",":","param","checkpoint_file",":","The","location","you","want","to","save","the","forecaster","."]
code: def save(self, checkpoint_file):
      """
      Save the forecaster.
  
      :param checkpoint_file: The location you want to save the forecaster.
      """
      if self.internal.model is None:
          from bigdl.nano.utils.common import invalidInputError
  
          invalidInputError(
              False,
              "You must call fit or restore first before calling save!",
          )
      self.internal.save(checkpoint_file)
  
file_path: BigDL/python/chronos/src/bigdl/chronos/forecaster/arima_forecaster.py
imports: from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.model.arima import ARIMAModel
label_desc: Use image node_id 6 for calling the ARIMAForecaster obj's underlying member method code with example usage: obj.save(checkpoint_file) without return types