docs: def eigenvectors_nrev(T, k, right=True, ncv=None):
      r"""Compute eigenvectors of transition matrix.
  
      Parameters
      ----------
      T : (M, M) scipy.sparse matrix
          Transition matrix (stochastic matrix)
      k : int
          Number of eigenvalues to compute
      right : bool, optional
          If True compute right eigenvectors, left eigenvectors otherwise
      ncv : int, optional
          The number of Lanczos vectors generated, `ncv` must be greater than k;
          it is recommended that ncv > 2*k
  
      Returns
      -------
      eigvec : (M, k) ndarray
          k-eigenvectors of T
  
      """
      if right:
          val, vecs = scipy.sparse.linalg.eigs(T, k=k, which='LM', ncv=ncv)
          ind = np.argsort(np.abs(val))[::-1]
          return vecs[:, ind]
      else:
          val, vecs = scipy.sparse.linalg.eigs(T.transpose(), k=k, which='LM', ncv=ncv)
          ind = np.argsort(np.abs(val))[::-1]
          return vecs[:, ind]