Add Prefix Parameter to lts for Tuning Spaces in Pipelines?

[giuseppec]

I wanted to use a search space for a pipeline and tried to rename the tuning space:

task = tsk("boston_housing")
tuner = tnr("random_search", batch_size = 3)

# Potentially large ML pipeline
xgb = as_learner(po("encode") %>>% lrn("regr.xgboost"))

search_space = lts("regr.xgboost.default")
# My attempt to rename the tuning space so that it matches the expected parameter names:
names(search_space$values) = paste0("regr.xgboost.", names(search_space$values)

at = auto_tuner(
  tuner = tuner,
  learner = xgb,
  search_space = search_space,
  resampling = rsmp("cv", folds = 2),
  measure = msr("regr.mse"),
  term_time = 20
)

at$train(task)

Maybe it could be useful to be able to allow passing a prefix to lts such as lts("regr.xgboost.default", param_set_prefix = "regr.xgboost.")?

The workaround is to use xgb = as_learner(po("encode") %>>% lts(lrn("regr.xgboost"))) maybe this can be mentioned in the book here?
Currently, the book only mentions that this is possible without mentioning a use case: "We could also apply the default search spaces from Bischl et al. (2023) by passing the learner to [lts()]".
Maybe one could add 1-2 more sentences to highlight that this can be useful if one wants to tune the learner parameters when the learner is combined with other pipeops?