parameters: (self)
docstring: If un-chunked data is available, set initial level to the first value.
docstring_summary: If un-chunked data is available, set initial level to the first value.
docstring_tokens: If un - chunked data is available set initial level to the first value .
function: def get_prior_mean(self):
      """If un-chunked data is available, set initial level to the first value."""
      with variable_scope.variable_scope(self._variable_scope):
        if self._input_statistics is not None:
          # TODO(allenl): Better support for multivariate series here.
          initial_value = array_ops.stack([
              math_ops.reduce_mean(
                  self._scale_data(
                      self._input_statistics.series_start_moments.mean)),
              0.
          ])
          return initial_value + variable_scope.get_variable(
              name="prior_state_mean",
              shape=initial_value.get_shape(),
              initializer=init_ops.zeros_initializer(),
              dtype=self.dtype,
              trainable=self._configuration.trainable_start_state)
        else:
          return super(AdderStateSpaceModel, self).get_prior_mean()
function_tokens: def get_prior_mean ( self ) : with variable_scope . variable_scope ( self . _variable_scope ) : if self . _input_statistics is not None : # TODO(allenl): Better support for multivariate series here. initial_value = array_ops . stack ( [ math_ops . reduce_mean ( self . _scale_data ( self . _input_statistics . series_start_moments . mean ) ) , 0. ] ) return initial_value + variable_scope . get_variable ( name = "prior_state_mean" , shape = initial_value . get_shape ( ) , initializer = init_ops . zeros_initializer ( ) , dtype = self . dtype , trainable = self . _configuration . trainable_start_state ) else : return super ( AdderStateSpaceModel , self ) . get_prior_mean ( )