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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 mv_normal_like(x, mu, tau):
 R""" Multivariate normal log-likelihood .. math:: f(x \mid \pi, T) = \frac{|T|^{1/2}}{(2\pi)^{1/2}} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}T(x-\mu) \right\} :Parameters: - `x` : (n,k) - `mu` : (k) Location parameter sequence. - `Tau` : (k,k) Positive definite precision matrix. .. seealso:: :func:`mv_normal_chol_like`, :func:`mv_normal_cov_like` """

    # TODO: Vectorize in Fortran
    if len(np.shape(x)) > 1:
        return np.sum([flib.prec_mvnorm(r, mu, tau) for r in x])
    else:
        return flib.prec_mvnorm(x, mu, tau)