 Prediction inequality from Lasso stationarity

For Lasso loss $\Vert Y-X\beta {\Vert }_2^2/(2n)+\lambda \Vert \beta {\Vert }_1$ and $Y=X{\beta }^0+\eta$, stationarity with a penalty subgradient gives $\Vert X\delta {\Vert }_2^2/n\le {\eta }^{T}X\delta /n+\lambda (\Vert {\beta }^0{\Vert }_1-\Vert \hat{\beta }{\Vert }_1)$, $\delta =\hat{\beta }-{\beta }^0$. This follows by multiplying the Karush-Kuhn-Tucker conditions by $\delta$ and using $z^T\hat{\beta }=\Vert \hat{\beta }{\Vert }_1$ and $z^T{\beta }^0\le \Vert {\beta }^0{\Vert }_1$.