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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 log_likelihood(covariance, precision):
 """Computes the log-likelihood between the covariance and precision estimate. Parameters covariance : 2D ndarray (n_features, n_features) Maximum Likelihood Estimator of covariance precision : 2D ndarray (n_features, n_features) The precision matrix of the covariance model to be tested Returns ------- log-likelihood """

    assert covariance.shape == precision.shape
    dim, _ = precision.shape
    log_likelihood_ = (
        -np.sum(covariance * precision)
        + fast_logdet(precision)
        - dim * np.log(2 * np.pi)
    )
    log_likelihood_ /= 2.
    return log_likelihood_