What is recommended way to incorporate both bootstrapping (i.e. to compute optimism in the R-squared value) and multiple imputation?

For example, is it acceptable to:

(1) Perform multiple imputation to generate x number of datasets (e.g. x = 10)

(2) Fit a pre-specified regression model separately to each of the 10 imputed datasets and then run a separate bootstrap for each dataset

(3) Report the average and range of optimism values (e.g. for R-squared) from the 10 bootstraps.

Any guidance would greatly be appreciated!