r/MachineLearning - [D] How much of an effect, if any, does batch size have when doing hyperparameter optimization?

I have been using sci-kit optimize to do hyperparameter search (using gp_minimize specifically) for a neural network. I am working on a binary classification problem with a significant class imbalance. I have been using a batch size of 10, but just came across a tweet and notebook by Francois Chollet where he recommended using a high batch size in class imbalance problems in order so that each batch contains at least a few positive examples. My question is can I just take the networks with the best network architectures I found via my hyperparameter search where I used a batch size of 32, but just retrain them using the same hyperparameters but using a higher batch size? Or, would batch size have a significant effect on hyperparameter optimization, and I would be better off just redoing hyperparameter optimization but this time with a larger batch size?