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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 _cholesky(self, A, **kwargs):
 """ method to handle potential problems with the cholesky decomposition. will try to increase L2 regularization of the penalty matrix to do away with non-positive-definite errors Parameters A : np.array Returns ------- np.array """

        # create appropriate-size diagonal matrix
        if sp.sparse.issparse(A):
            diag = sp.sparse.eye(A.shape[0])
        else:
            diag = np.eye(A.shape[0])

        constraint_l2 = self._constraint_l2
        while constraint_l2 <= self._constraint_l2_max:
            try:
                L = cholesky(A, **kwargs)
                self._constraint_l2 = constraint_l2
                return L
            except NotPositiveDefiniteError:
                if self.verbose:
                    warnings.warn('Matrix is not positive definite. \n'\
                                  'Increasing l2 reg by factor of 10.',
                                  stacklevel=2)
                A -= constraint_l2 * diag
                constraint_l2 *= 10
                A += constraint_l2 * diag

        raise NotPositiveDefiniteError('Matrix is not positive \n'
                                       'definite.')