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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 compute_eigen(self, n_comps=15, sym=None, sort='decrease'):
 """Compute eigen decomposition of transition matrix. Parameters n_comps : `int` Number of eigenvalues/vectors to be computed, set `n_comps = 0` if you need all eigenvectors. sym : `bool` Instead of computing the eigendecomposition of the assymetric transition matrix, computed the eigendecomposition of the symmetric Ktilde matrix. matrix : sparse matrix, np.ndarray, optional (default: `.connectivities`) Matrix to diagonalize. Merely for testing and comparison purposes. Returns ------- Writes the following attributes. eigen_values : numpy.ndarray Eigenvalues of transition matrix. eigen_basis : numpy.ndarray Matrix of eigenvectors (stored in columns). `.eigen_basis` is projection of data matrix on right eigenvectors, that is, the projection on the diffusion components. these are simply the components of the right eigenvectors and can directly be used for plotting. """

        np.set_printoptions(precision=10)
        if self._transitions_sym is None:
            raise ValueError('Run `.compute_transitions` first.')
        matrix = self._transitions_sym
        # compute the spectrum
        if n_comps == 0:
            evals, evecs = scipy.linalg.eigh(matrix)
        else:
            n_comps = min(matrix.shape[0]-1, n_comps)
            # ncv = max(2 * n_comps + 1, int(np.sqrt(matrix.shape[0])))
            ncv = None
            which = 'LM' if sort == 'decrease' else 'SM'
            # it pays off to increase the stability with a bit more precision
            matrix = matrix.astype(np.float64)
            evals, evecs = scipy.sparse.linalg.eigsh(matrix, k=n_comps,
                                                  which=which, ncv=ncv)
            evals, evecs = evals.astype(np.float32), evecs.astype(np.float32)
        if sort == 'decrease':
            evals = evals[::-1]
            evecs = evecs[:, ::-1]
        logg.info('    eigenvalues of transition matrix\n'
                  '    {}'.format(str(evals).replace('\n', '\n    ')))
        if self._number_connected_components > len(evals)/2:
            logg.warn('Transition matrix has many disconnected components!')
        self._eigen_values = evals
        self._eigen_basis = evecs