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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 ebic(covariance, precision, n_samples, n_features, gamma=0):
 """ Extended Bayesian Information Criteria for model selection. When using path mode, use this as an alternative to cross-validation for finding lambda. See: "Extended Bayesian Information Criteria for Gaussian Graphical Models" R. Foygel and M. Drton, NIPS 2010 Parameters covariance : 2D ndarray (n_features, n_features) Maximum Likelihood Estimator of covariance (sample covariance) precision : 2D ndarray (n_features, n_features) The precision matrix of the model to be tested n_samples : int Number of examples. n_features : int Dimension of an example. lam: (float) Threshold value for precision matrix. This should be lambda scaling used to obtain this estimate. gamma : (float) \in (0, 1) Choice of gamma=0 leads to classical BIC Positive gamma leads to stronger penalization of large graphs. Returns ------- ebic score (float). Caller should minimized this score. """

    l_theta = -np.sum(covariance * precision) + fast_logdet(precision)
    l_theta *= n_features / 2.

    # is something goes wrong with fast_logdet, return large value
    if np.isinf(l_theta) or np.isnan(l_theta):
        return 1e10

    mask = np.abs(precision.flat) > np.finfo(precision.dtype).eps
    precision_nnz = (np.sum(mask) - n_features) / 2.0  # lower off diagonal tri

    return (
        -2.0 * l_theta
        + precision_nnz * np.log(n_samples)
        + 4.0 * precision_nnz * np.log(n_features) * gamma
    )