Robustifying standard errrors by cluster variable in logistic regression

[redecker-m]

I need to adapt my logistic regression model for making robust standard errors with respect to a clustering variable (e.g. having data on employees but additional explanatory variables referring to the company). In Stata, this is a direct option 'VCE cluster' applied on glm model.
In R there seem to be very different libraries dealing with that problem, like 'sandwich'+ 'lmtest' or 'rms' or 'miceadds', but all dealing with a singular alternated 'glm' model - instead of a combined learner-task scheme.
For examples, see:
https://github.com/danilofreire/r-scripts/blob/master/robust-se-for-lm-and-logit.R
https://stackoverflow.com/questions/16498849/logistic-regression-with-robust-clustered-standard-errors-in-r

I did not see any option for 'classif.logreg' to include robustness by cluster variables, neither a feature in 'mlr3pipelines'. Is there any way to deal with it directly in mlr3?