Nested CV is Memory-inefficient

Hello,

I noticed that when using nested_cv (or bringing over a custom nested resampling scheme via caret2rsample(), the memory does scale linearly with the number of folds. This is in contrast to a single resampling (for example the bootstrap resampling example on the rsample documentation landing page), where a feature of the package is that the memory doesn't blow up when resampling a dataset many times.

I adapted that example to show what I'm talking about:

library(rsample)
library(mlbench)

data(LetterRecognition)
lobstr::obj_size(LetterRecognition)
# 2,644,640 B

set.seed(35222)
#For an example, an outer 5 fold cv with each outer fold having an inner 2 fold cv
nested <- rsample::nested_cv(LetterRecognition, outside = vfold_cv(times = 5),inside=vfold_cv(times = 2))
lobstr::obj_size(nested)
#34,434,200 B

# Object size per resample - Actually slightly bigger than what we started with
lobstr::obj_size(nested)/nrow(nested)
#3,443,420 B


# Fold increase is > 10
as.numeric(lobstr::obj_size(nested)/lobstr::obj_size(LetterRecognition))
#13.02037

Unless I'm missing something, nested resampling on a large dataset doesn't seem to be possible, since the memory required will quickly add up.

Start with the nested_cv() example using LetterRecognition, vfold_cv(), and lobstr::obj_size(), then compare it with the bootstrap resampling example linked in the issue. Investigate how nested resamples retain data and verify the result by measuring object size as the number of folds changes; done means nested resampling no longer scales linearly in memory.