class_docstr: A base class for forecast models. Fits a timeseries and predicts future values
  
  Parameters
  ----------
  score_func : callable, optional, default=mean_squared_error
      Function to calculate model R2_null_model_score score,
      with signature (actual, predicted).
      `actual`, `predicted` are array-like with the same shape.
      Smaller values are better.
  
  coverage : float, optional, default=0.95
      intended coverage of the prediction bands (0.0 to 1.0)
      If None, the upper/lower predictions are not returned by `predict`
  
      Every subclass must use `coverage` to set prediction band width. This ensures a common
      BaseForecastEstimator interface for parameters used during fitting and forecast evaluation
  
  null_model_params : dict with arguments passed to DummyRegressor, optional, default=None
      Dictionary keys must be in ("strategy", "constant", "quantile")
      Defines null model. model score is R2_null_model_score of model error relative to null model, evaluated by score_func
      If None, model score is score_func of the model itself
  
  Attributes
  ----------
  null_model : DummyEstimator
      null model used to measure model score
  time_col_ : str
      Name of input data time column
  value_col_ : str
      Name of input data value column
  last_predicted_X_ : `pandas.DataFrame` or None
      The ``X`` last passed to ``self.predict()``.
      Used to speed up predictions if the same ``X`` is passed repeatedly.
      Resets to None when ``self.fit()`` is called.
  cached_predictions_ : `pandas.DataFrame` or None
      The return value of the last call to ``self.predict()``.
      Used to speed up predictions if the same ``X`` is passed repeatedly.
      Resets to None when ``self.fit()`` is called.
class_docstr_tok: ["A","base","class","for","forecast","models",".","Fits","a","timeseries","and","predicts","future","values","Parameters","--","--","--","--","--","score_func",":","callable",",","optional",",","default=mean_squared_error","Function","to","calculate","model","R2_null_model_score","score",",","with","signature","(","actual",",","predicted",")",".","`","actual","`",",","`","predicted","`","are","array-like","with","the","same","shape",".","Smaller","values","are","better",".","coverage",":","float",",","optional",",","default=0.95","intended","coverage","of","the","prediction","bands","(","0.0","to","1.0",")","If","None",",","the","upper\/lower","predictions","are","not","returned","by","`","predict","`","Every","subclass","must","use","`","coverage","`","to","set","prediction","band","width",".","This","ensures","a","common","BaseForecastEstimator","interface","for","parameters","used","during","fitting","and","forecast","evaluation","null_model_params",":","dict","with","arguments","passed","to","DummyRegressor",",","optional",",","default=None","Dictionary","keys","must","be","in","(","``","strategy","''",",","``","constant","''",",","``","quantile","''",")","Defines","null","model",".","model","score","is","R2_null_model_score","of","model","error","relative","to","null","model",",","evaluated","by","score_func","If","None",",","model","score","is","score_func","of","the","model","itself","Attributes","--","--","--","--","--","null_model",":","DummyEstimator","null","model","used","to","measure","model","score","time_col_",":","str","Name","of","input","data","time","column","value_col_",":","str","Name","of","input","data","value","column","last_predicted_X_",":","`","pandas.DataFrame","`","or","None","The","``","X","``","last","passed","to","``","self.predict","(",")","``",".","Used","to","speed","up","predictions","if","the","same","``","X","``","is","passed","repeatedly",".","Resets","to","None","when","``","self.fit","(",")","``","is","called",".","cached_predictions_",":","`","pandas.DataFrame","`","or","None","The","return","value","of","the","last","call","to","``","self.predict","(",")","``",".","Used","to","speed","up","predictions","if","the","same","``","X","``","is","passed","repeatedly",".","Resets","to","None","when","``","self.fit","(",")","``","is","called","."]
docstr: If any parameters of the ``uncertainty_dict`` for specific uncertainty methods
  need to be populated from the estimator, they should be added here.
  
  Parameters
  ----------
  uncertainty_dict : `dict` [`str`, any]
      The original uncertainty dictionary.
  
  Returns
  -------
  uncertainty_dict : `dict` [`str`, any]
      The uncertainty dictionary with additional parameters populated from the estimator.
docstr_tok: ["If","any","parameters","of","the","``","uncertainty_dict","``","for","specific","uncertainty","methods","need","to","be","populated","from","the","estimator",",","they","should","be","added","here",".","Parameters","--","--","--","--","--","uncertainty_dict",":","`","dict","`","[","`","str","`",",","any","]","The","original","uncertainty","dictionary",".","Returns","--","--","--","-","uncertainty_dict",":","`","dict","`","[","`","str","`",",","any","]","The","uncertainty","dictionary","with","additional","parameters","populated","from","the","estimator","."]
code: def _populate_uncertainty_params(self, uncertainty_dict: dict):
      """If any parameters of the ``uncertainty_dict`` for specific uncertainty methods
      need to be populated from the estimator, they should be added here.
  
      Parameters
      ----------
      uncertainty_dict : `dict` [`str`, any]
          The original uncertainty dictionary.
  
      Returns
      -------
      uncertainty_dict : `dict` [`str`, any]
          The uncertainty dictionary with additional parameters populated from the estimator.
      """
      if (
          uncertainty_dict.get("uncertainty_method")
          == UncertaintyMethodEnum.simple_conditional_residuals.name
      ):
          # Populates ``value_col`` and ``residual_col`` for "simple_conditional_residuals" method.
          # The method always uses residual based prediction intervals.
          # If ``value_col`` is not in ``params``, ``self.value_col_`` will be used.
          # If ``residual_col`` is not in ``params``,
          # a residual column will be calculated based on the ``cst.PREDICT_COL``.
          # The algorithm assumes there's always ``cst.PREDICT_COL`` in the input df.
          params = uncertainty_dict.get("params", {})
          if "value_col" not in params:
              params["value_col"] = self.value_col_
          if "residual_col" not in params:
              params["residual_col"] = "residual_col"
          uncertainty_dict["params"] = params
          return uncertainty_dict
      if (
          uncertainty_dict.get("uncertainty_method")
          == UncertaintyMethodEnum.quantile_regression.name
      ):
          params = uncertainty_dict.get("params", {})
          if "value_col" not in params:
              params["value_col"] = self.value_col_
          uncertainty_dict["params"] = params
          return uncertainty_dict
  
imports: from abc import ABC
  from abc import abstractmethod
  from typing import Optional
  import pandas
  from sklearn.base import BaseEstimator
  from sklearn.base import RegressorMixin
  from sklearn.metrics import mean_squared_error
  from greykite.common import constants
  from greykite.common.evaluation import r2_null_model_score
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
  from greykite.sklearn.estimator.null_model import DummyEstimator
  from greykite.sklearn.uncertainty.exceptions import UncertaintyError
  from greykite.sklearn.uncertainty.uncertainty_methods import UncertaintyMethodEnum
label_desc: Use image node_id 8 for calling the BaseForecastEstimator obj's underlying member method code with example usage: obj._populate_uncertainty_params(uncertainty_dict) and returns: uncertainty_dict, uncertainty_dict