[differential_privacy] Learning rates used for Adaptive Clipping experiments

Hi,

I am trying to reproduce the experiments in ["Differentially Private Learning with Adaptive Clipping" (2021)](https://arxiv.org/abs/1905.03871), the source code for which is provided under `federated/differential_privacy`. The paper does not report the final server learning rates used for DP-FedAvgM with clipping enabled. It simply states the following in Section 3.1 -

_Therefore, for all approaches with clipping—fixed or adaptive—we search over a small grid of five server learning rates, scaling the
values in Table 1 by {1, 10^1/4, 10^1/2, 10^3/4, 10}. For all configurations, we report the best performing model whose server learning rate was chosen from this small grid on the validation set._

It is not computationally feasible for me to search for the optimal server lr in every possible configuration so I was hoping you could specify the learning rates that were used for training the best performing models. Thank you.