I'm trying to learn how to implement MICE in imputing missing values for my datasets. I've heard about fancyimpute's MICE, but I also read that sklearn's IterativeImputer class can accomplish similar results. From sklearn's docs:

> 
> Our implementation of IterativeImputer was inspired by the R MICE
>  package (Multivariate Imputation by Chained Equations) [1], but
>  differs from it by returning a single imputation instead of multiple
>  imputations. However, IterativeImputer can also be used for multiple
>  imputations by applying it repeatedly to the same dataset with
>  different random seeds when sample\_posterior=True
> 
> 
> 

I've seen "seeds" being used in different pipelines, but I never understood them well enough to implement them in my own code. **I was wondering if anyone could explain and provide an example on how to implement seeds for a MICE imputation using sklearn's IterativeImputer?** Thanks!