def get_log_norm_const(self):
    D = self.D
    a = self.a
    b = self.b
    logNormConstNormal = 0.5 * D * (LOGTWOPI - np.log(self.kappa))
    logNormConstGamma  = np.sum(gammaln(a)) - np.inner(a, np.log(b))
    return logNormConstNormal + logNormConstGamma
The upper bounds for the problem
dataset/ETHPy150Open daeilkim/refinery/refinery/bnpy/bnpy-dev/bnpy/distr/GaussGammaDistr.py/GaussGammaDistr.get_log_norm_const--crossover with--dataset/ETHPy150Open scipy/scipy/benchmarks/benchmarks/go_benchmark_functions/go_benchmark.py/Benchmark.xmax