parameters: (self,
                   learning_rate=0.001,
                   clip=0.9,
                   batch_size=0.999,
                   sigma=1e-8,
                   parameter_list=None)
docstring: Note(wangzhongpu):
          This property is only used for debugging, do not need to set it!
          Dpsgd operator use time(NULL) as random seed to generate random number.
          However, during debugging, we need determinated result, so we will set self._seed to a fixed number.
docstring_summary: Note(wangzhongpu):
          This property is only used for debugging, do not need to set it!
          Dpsgd operator use time(NULL) as random seed to generate random number.
          However, during debugging, we need determinated result, so we will set self._seed to a fixed number.
docstring_tokens: Note ( wangzhongpu ) : This property is only used for debugging do not need to set it! Dpsgd operator use time ( NULL ) as random seed to generate random number . However during debugging we need determinated result so we will set self . _seed to a fixed number .
function: def __init__(self,
                   learning_rate=0.001,
                   clip=0.9,
                   batch_size=0.999,
                   sigma=1e-8,
                   parameter_list=None):
          assert learning_rate is not None
          assert clip is not None
          assert batch_size is not None
          assert sigma is not None
          super(DpsgdOptimizer, self).__init__(
              learning_rate=learning_rate, parameter_list=parameter_list)
          self.type = "dpsgd"
          self._clip = clip
          self._batch_size = batch_size
          self._sigma = sigma
          '''
          Note(wangzhongpu):
          This property is only used for debugging, do not need to set it!
          Dpsgd operator use time(NULL) as random seed to generate random number.
          However, during debugging, we need determinated result, so we will set self._seed to a fixed number.
          '''
          self._seed = None
function_tokens: def __init__ ( self , learning_rate = 0.001 , clip = 0.9 , batch_size = 0.999 , sigma = 1e-8 , parameter_list = None ) : assert learning_rate is not None assert clip is not None assert batch_size is not None assert sigma is not None super ( DpsgdOptimizer , self ) . __init__ ( learning_rate = learning_rate , parameter_list = parameter_list ) self . type = "dpsgd" self . _clip = clip self . _batch_size = batch_size self . _sigma = sigma self . _seed = None