Is it possible to retrieve an imputed dataset instead of pooled estimates using the mice package in R for multiple imputation?

Question

I want to use multiple imputation using the mice package in R to deal with missing values. I am relatively new to this and had trouble understanding mice completely. Now, I understand that usually one does not retrieve one "final" imputed dataset, but rather pooled estimates which were retrieved from the m imputed datasets. However, for my use case I want exactly that, one final dataset with values that I somehow retrieved from the mice imputations. This final dataset should then be used to assess the imputation quality and compare it to other imputation techniques.

What I did previously was just averaging the values in the m imputed datasets which I know now defeats the purpose of multiple imputation. So is there are way to do this while still keeping the principle of mice in mind?

Using this example from https://stefvanbuuren.name/fimd/workflow.html:

# mids workflow using pipes
library(magrittr)
est2 <- nhanes %>%
  mice(seed = 123, print = FALSE) %>%
  with(lm(chl ~ age + bmi + hyp)) %>%
  pool()

This code results in pooled estimates which are retrieved from the imputed datasets. Is it possible to use these estimates to retrieve an imputed dataset or is there any other way? I would like to be able to retrieve values for age, bmi and hyp I can use to impute the missing values in the dataset.