The following quote is extracted from Dr seaman article "Combining Multiple Imputation and Inverse-Probability Weighting". Could you please explain more?

"Another possibility is to combine MI and IPW. A rule is specified for when to include an individual in the analysis: e.g., if they attended a follow-up visit, or if more than a certain percentage of their data is observed. Missing values in included individuals are multiply imputed and each resulting dataset (which we call a “quasi-complete dataset” because the data are complete for the included, but not excluded, individuals) is analyzed using IPW to account for the exclusion of individuals not satisfying the inclusion rule and for different sampling fractions (if any). The “quasi-complete-data” estimator θ is then the IPW estimator using the data on included individuals in a single quasi-complete dataset..."

I think, first i should run MI only on participants meeting this threshold and then use IPW on all participants (both included and excluded). if yes, how can i calculate these weights?