dbplyr, dplyr, and functions with no SQL equivalents [eg `slice()`]

library(tidyverse)
con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
copy_to(con, mtcars)
mtcars2 <- tbl(con, "mtcars")

I can create this mock SQL database above. And it's very cool that I can perform standard dplyr functions on this "database":

mtcars2 %>% 
  group_by(cyl) %>% 
  summarise(mpg = mean(mpg, na.rm = TRUE)) %>% 
  arrange(desc(mpg))
#> # Source:     lazy query [?? x 2]
#> # Database:   sqlite 3.29.0 [:memory:]
#> # Ordered by: desc(mpg)
#>     cyl   mpg
#>   <dbl> <dbl>
#> 1     4  26.7
#> 2     6  19.7
#> 3     8  15.1

It appears I'm unable to use dplyr functions that have no direct SQL equivalents, (eg dplyr::slice()). In the case of slice() I can use the alternative combination of filter() and row_number() to get the same results as just using slice(). But what happens when there's not such an easy workaround?

mtcars2 %>% slice(1:5)
#>Error in UseMethod("slice_") : 
#>  no applicable method for 'slice_' applied to an object of class 
#>  "c('tbl_SQLiteConnection', 'tbl_dbi', 'tbl_sql', 'tbl_lazy', 'tbl')"

When dplyr functions have no direct SQL equivalents can I force their use with dbplyr, or is the only option to get creative with dplyr verbs that do have SQL equivalents, or just write the SQL directly (which is not my preferred solution)?