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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 kl_loss(covariance, precision):
 """Computes the KL divergence between precision estimate and reference covariance. The loss is computed as: Trace(Theta_1 * Sigma_0) - log(Theta_0 * Sigma_1) - dim(Sigma) 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 ------- KL-divergence """

    assert covariance.shape == precision.shape
    dim, _ = precision.shape
    logdet_p_dot_c = fast_logdet(np.dot(precision, covariance))
    return 0.5 * (np.sum(precision * covariance) - logdet_p_dot_c - dim)