How to transform this data, map() to tune()

Question

An iterative process is used to compare machine learning models.

I was able to find the k-neighbor method in parsnip, but can I modify it to k-means to create something similar to the above code?
Do you have any information that might be useful?

To make the code easier to understand, I want to tune the K using tune().

poi <- tibble(
  cluster = factor(1:3),num_points = c(100, 150, 50),
  x1 = c(5, 0, -3),x2 = c(-1, 1, -2))%>%
  mutate(
    x1 = map2(num_points, x1, rnorm),
    x2 = map2(num_points, x2, rnorm)) %>% 
  unnest(cols = c(x1, x2)) %>%
  select(-cluster,-num_points)

res <- 
  tibble(k = 1:9) %>%
  mutate(
    kclust = map(k, ~kmeans(poi, .x)),
    augmented = map(kclust, augment, poi)
  )