parameters: (estimator)
return_statement: return isinstance(estimator, (DPExplainableBoostingClassifier, DPExplainableBoostingRegressor))
docstring: Return True if the given estimator is a differentially private EBM estimator
      Parameters
      ----------
      estimator : estimator instance
          Estimator object to test.
      Returns
      -------
      out : bool
          True if estimator is a differentially private EBM estimator and False otherwise.
docstring_summary: Return True if the given estimator is a differentially private EBM estimator
      Parameters
      ----------
      estimator : estimator instance
          Estimator object to test.
      Returns
      -------
      out : bool
          True if estimator is a differentially private EBM estimator and False otherwise.
docstring_tokens: Return True if the given estimator is a differentially private EBM estimator Parameters ---------- estimator : estimator instance Estimator object to test . Returns ------- out : bool True if estimator is a differentially private EBM estimator and False otherwise .
function: def is_private(estimator):
      """Return True if the given estimator is a differentially private EBM estimator
      Parameters
      ----------
      estimator : estimator instance
          Estimator object to test.
      Returns
      -------
      out : bool
          True if estimator is a differentially private EBM estimator and False otherwise.
      """
  
      return isinstance(estimator, (DPExplainableBoostingClassifier, DPExplainableBoostingRegressor))
function_tokens: def is_private ( estimator ) : return isinstance ( estimator , ( DPExplainableBoostingClassifier , DPExplainableBoostingRegressor ) )