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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_cov_like(x, mu, C):
 R""" Multivariate normal log-likelihood parameterized by a covariance matrix. .. math:: f(x \mid \pi, C) = \frac{1}{(2\pi|C|)^{1/2}} \exp\left\{ -\frac{1}{2} (x-\mu)^{\prime}C^{-1}(x-\mu) \right\} :Parameters: - `x` : (n,k) - `mu` : (k) Location parameter. - `C` : (k,k) Positive definite covariance matrix. .. seealso:: :func:`mv_normal_like`, :func:`mv_normal_chol_like` """

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