class_docstr: Wrapper for Facebook Prophet model.
  
  Parameters
  ----------
  score_func : callable
      see BaseForecastEstimator
  
  coverage : float between [0.0, 1.0]
      see BaseForecastEstimator
  
  null_model_params : dict with arguments to define DummyRegressor null model, optional, default=None
      see BaseForecastEstimator
  
  add_regressor_dict: dictionary of extra regressors to be added to the model, optional, default=None
      These should be available for training and entire prediction interval.
  
      Dictionary format::
  
          add_regressor_dict={  # we can add as many regressors as we want, in the following format
              "reg_col1": {
                  "prior_scale": 10,
                  "standardize": True,
                  "mode": 'additive'
              },
              "reg_col2": {
                  "prior_scale": 20,
                  "standardize": True,
                  "mode": 'multiplicative'
              }
          }
  
  add_seasonality_dict: dict of custom seasonality parameters to be added to the model, optional, default=None
      parameter details: https://github.com/facebook/prophet/blob/main/python/prophet/forecaster.py - refer to
      add_seasonality() function.
      Key is the seasonality component name e.g. 'monthly'; parameters are specified via dict.
  
      Dictionary format::
  
          add_seasonality_dict={
              'monthly': {
                  'period': 30.5,
                  'fourier_order': 5
              },
              'weekly': {
                  'period': 7,
                  'fourier_order': 20,
                  'prior_scale': 0.6,
                  'mode': 'additive',
                  'condition_name': 'condition_col'  # takes a bool column in df with True/False values. This means that
                  # the seasonality will only be applied to dates where the condition_name column is True.
              },
              'yearly': {
                  'period': 365.25,
                  'fourier_order': 10,
                  'prior_scale': 0.2,
                  'mode': 'additive'
              }
          }
  
      Note: If there is a conflict in built-in and custom seasonality e.g. both have "yearly", then custom seasonality
      will be used and Model will throw a warning such as:
      "INFO:prophet:Found custom seasonality named "yearly", disabling built-in yearly seasonality."
  
  kwargs : additional parameters
      Other parameters are the same as Prophet model, with one exception:
      ``interval_width`` is specified by ``coverage``.
  
      See source code ``__init__`` for the parameter names, and refer to
      Prophet documentation for a description:
  
          * https://facebook.github.io/prophet/docs/quick_start.html
          * https://github.com/facebook/prophet/blob/main/python/prophet/forecaster.py
  
  Attributes
  ----------
  model : ``Prophet`` object
      Prophet model object
  forecast : `pandas.DataFrame`
      Output of predict method of ``Prophet``.
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docstr: Prints input parameters and Prophet model parameters.
  
  Returns
  -------
  log_message : str
      log message printed to logging.info()
docstr_tok: ["Prints","input","parameters","and","Prophet","model","parameters",".","Returns","--","--","--","-","log_message",":","str","log","message","printed","to","logging.info","(",")"]
code: def summary(self):
      """Prints input parameters and Prophet model parameters.
  
      Returns
      -------
      log_message : str
          log message printed to logging.info()
      """
      super().summary()
      if self.model is not None:
          log_message(pprint(self.model.params), LoggingLevelEnum.INFO)
  
file_path: greykite/greykite/sklearn/estimator/prophet_estimator.py
imports: import sys
  from sklearn.exceptions import NotFittedError
  from sklearn.metrics import mean_squared_error
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
  from greykite.common.logging import pprint
  from greykite.sklearn.estimator.base_forecast_estimator import BaseForecastEstimator
label_desc: Use image node_id 4 for calling the ProphetEstimator obj's underlying member method code with example usage: obj.summary() without return types