class_docstr: Specifies the parameters for a DP mechanism.
  
  MechanismType defines the kind of noise distribution.
  _noise_standard_deviation is the minimized noise standard deviation.
  (_eps, _delta) are parameters of (eps, delta)-differential privacy
class_docstr_tok: ["Specifies","the","parameters","for","a","DP","mechanism",".","MechanismType","defines","the","kind","of","noise","distribution",".","_noise_standard_deviation","is","the","minimized","noise","standard","deviation",".","(","_eps",",","_delta",")","are","parameters","of","(","eps",",","delta",")","-differential","privacy"]
docstr: Parameter of (eps, delta)-differential privacy.
  Raises:
      AssertionError: The privacy budget is not calculated yet.
docstr_tok: ["Parameter","of","(","eps",",","delta",")","-differential","privacy",".","Raises",":","AssertionError",":","The","privacy","budget","is","not","calculated","yet","."]
code: def eps(self):
      """Parameter of (eps, delta)-differential privacy.
      Raises:
          AssertionError: The privacy budget is not calculated yet.
      """
      if self._eps is None:
          raise AssertionError("Privacy budget is not calculated yet.")
      return self._eps
  
imports: import abc
  import collections
  import logging
  import math
  from typing import Optional
  from dataclasses import dataclass
  import pipeline_dp.aggregate_params
  from pipeline_dp import input_validators
label_desc: Use image node_id 2 for calling the MechanismSpec obj's underlying member method code with example usage: obj.eps() and returns: self