<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def ebic(self, gamma=0):
 """Compute EBIC scores for each model. If model is not "path" then returns a scalar score value. May require self.path_ See: Extended Bayesian Information Criteria for Gaussian Graphical Models R. Foygel and M. Drton NIPS 2010 Parameters gamma : (float) \in (0, 1) Choice of gamma=0 leads to classical BIC Positive gamma leads to stronger penalization of large graphs. Returns ------- Scalar ebic score or list of ebic scores. """

        if not self.is_fitted_:
            return

        if not isinstance(self.precision_, list):
            return metrics.ebic(
                self.sample_covariance_,
                self.precision_,
                self.n_samples_,
                self.n_features_,
                gamma=gamma,
            )

        ebic_scores = []
        for lidx, lam in enumerate(self.path_):
            ebic_scores.append(
                metrics.ebic(
                    self.sample_covariance_,
                    self.precision_[lidx],
                    self.n_samples_,
                    self.n_features_,
                    gamma=gamma,
                )
            )

        return np.array(ebic_scores)