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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def arlognormal_like(x, a, sigma, rho):
 R""" Autoregressive lognormal log-likelihood. .. math:: x_i & = a_i \exp(e_i) \\ e_i & = \rho e_{i-1} + \epsilon_i where :math:`\epsilon_i \sim N(0,\sigma)`. """

    return flib.arlognormal(x, np.log(a), sigma, rho, beta=1)