class_docstr: Example:
      >>> #The dataset is split into data, validation_data
      >>> model = ProphetForecaster(changepoint_prior_scale=0.05, seasonality_mode='additive')
      >>> model.fit(data, validation_data)
      >>> predict_result = model.predict(horizon=24)
class_docstr_tok: ["Example",":",">",">",">","#","The","dataset","is","split","into","data",",","validation_data",">",">",">","model","=","ProphetForecaster","(","changepoint_prior_scale=0.05",",","seasonality_mode='additive","'",")",">",">",">","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, should be a json file
docstr_tok: ["Save","the","forecaster",".",":","param","checkpoint_file",":","The","location","you","want","to","save","the","forecaster",",","should","be","a","json","file"]
code: def save(self, checkpoint_file):
      """
      Save the forecaster.
  
      :param checkpoint_file: The location you want to save the forecaster, should be a json file
      """
      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/prophet_forecaster.py
imports: from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.model.prophet import ProphetModel
label_desc: Use image node_id 6 for calling the ProphetForecaster obj's underlying member method code with example usage: obj.save(checkpoint_file) without return types