#NEWLINE# def gradient( self , theta ) : #NEWLINE# #INDENT# '\n        Computes the gradient of the prior with\n        respect to theta.\n\n        Parameters\n        ----------\n        theta : (D,) numpy array\n            Hyperparameter configuration in log space\n\n        Returns\n        -------\n        (D) np.array\n            The gradient of the prior at theta.\n        ' #NEWLINE# return ( ( 1 / ( self . sigma * np . sqrt ( ( 2 * np . pi ) ) ) ) * ( ( ( - theta ) / ( self . sigma ** 2 ) ) * np . exp ( ( ( - ( theta ** 2 ) ) / ( 2 * ( self . sigma ** 2 ) ) ) ) ) )