code: def cho_factor(A, rho, lower=False, check_finite=True):
      r
  
      N, M = A.shape
      # If N < M it is cheaper to factorise A*A^T + rho*I and then use the
      # matrix inversion lemma to compute the inverse of A^T*A + rho*I
      if N >= M:
          c, lwr = linalg.cho_factor(
              A.T.dot(A) + rho * np.identity(M, dtype=A.dtype), lower=lower,
              check_finite=check_finite)
      else:
          c, lwr = linalg.cho_factor(
              A.dot(A.T) + rho * np.identity(N, dtype=A.dtype), lower=lower,
              check_finite=check_finite)
      return c, lwr
docs: r"""
      Compute Cholesky factorisation of either :math:`A^T A + \rho I` or
      :math:`A A^T + \rho I`, depending on which matrix is smaller.
  
      Parameters
      ----------
      A : array_like
        Array :math:`A`
      rho : float
        Scalar :math:`\rho`
      lower : bool, optional (default False)
        Flag indicating whether lower or upper triangular factors are
        computed
      check_finite : bool, optional (default False)
        Flag indicating whether the input array should be checked for Inf
        and NaN values
  
      Returns
      -------
      c : ndarray
        Matrix containing lower or upper triangular Cholesky factor,
        as returned by :func:`scipy.linalg.cho_factor`
      lwr : bool
        Flag indicating whether the factor is lower or upper triangular