human
Solve the following python programming problem given the following code:
def black_box_variational_inference(logprob, D, num_samples) : 
    'Implements http://arxiv.org/abs/1401.0118, and uses the
    local reparameterization trick from http://arxiv.org/abs/1506.02557' 
    def unpack_params(params) : 
        (mean, log_std) = (params [: D], params [D :]) 
        return (mean, log_std) 
def gaussian_entropy(log_std) : 
        return (((0.5 * D) * (1.0 + np.log ((2 * np.pi)))) + np.sum (log_std)) 
rs = npr.RandomState (0) 
    def variational_objective(params, t) : 
        'Provides a stochastic estimate of the variational lower bound.' 
        (mean, log_std) = unpack_params (params) 
        samples = ((rs.randn (num_samples, D) * np.exp (log_std)) + mean) 
        lower_bound = (gaussian_entropy (log_std) + np.mean (logprob (samples, t))) 
        return (- lower_bound) 
gradient = grad (variational_objective) 
    return (rs, gradient, unpack_params) 

    
    

    Fix the buggy line: return (rs, gradient, unpack_params)
assistant
#Here is Python program to fix the buggy line: return (rs, gradient, unpack_params)
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return (variational_objective, gradient, unpack_params)