def gradients_XX_diag(self, dL_dK_diag, X):
dL_dK_diag = dL_dK_diag.copy().reshape(-<NUM_LIT:1>, <NUM_LIT:1>, <NUM_LIT:1>)<EOL>assert (dL_dK_diag.size == X.shape[<NUM_LIT:0>]) or (dL_dK_diag.size == <NUM_LIT:1>), "<STR_LIT>"<EOL>l4 =  np.ones(X.shape[<NUM_LIT:1>])*self.lengthscale**<NUM_LIT:2><EOL>return dL_dK_diag * (np.eye(X.shape[<NUM_LIT:1>]) * -self.dK2_drdr_diag()/(l4))[None, :,:]<EOL>
Given the derivative of the objective dL_dK, compute the second derivative of K wrt X:

..math:
  \frac{\partial^2 K}{\partial X\partial X}

..returns:
    dL2_dXdX: [NxQxQ]